AICL/datasets/aicl_complete_dataset.jsonl at main · AFKmoney/AICL

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Add AICL training dataset: 43 examples across 43 domains, all 10 levels

Jun 13, 2026

622974a · Jun 13, 2026

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{\"id\": \"aicl_001\", \"domain\": \"ecommerce\", \"levels\": [1,2,3,4,5,6,7,9], \"description\": \"Build an e-commerce platform with product catalog, shopping cart, order processing, payment, and inventory management\", \"code\": \"# E-Commerce Platform\\n# Levels 1-7, 9: Full shopping platform with security\\n\\nGoal:\\nCreate an e-commerce platform with product catalog, shopping cart, order processing, payment handling, and inventory management\\n\\nConstraint:\\nAll payment data must be PCI-DSS compliant\\n\\nConstraint:\\nProduct search must return results within 100ms\\n\\nConstraint:\\nSystem must handle 5000 concurrent shoppers\\n\\nRisk:\\nPayment processing failure\\n\\nRecovery:\\nRetry payment with backup gateway and queue order for manual review\\n\\nRisk:\\nOverselling products out of stock\\n\\nRecovery:\\nReserve inventory at cart addition and release on timeout\\n\\nRisk:\\nCart data lost during checkout\\n\\nRecovery:\\nPersist cart state to database before each checkout step\\n\\nLayer:\\n    Catalog\\n    SubLayer:\\n        Products\\n    SubLayer:\\n        Search\\n\\nLayer:\\n    Cart\\n    SubLayer:\\n        Items\\n    SubLayer:\\n        Pricing\\n\\nLayer:\\n    Order\\n    SubLayer:\\n        Checkout\\n    SubLayer:\\n        Fulfillment\\n\\nLayer:\\n    Payment\\n    SubLayer:\\n        Processing\\n    SubLayer:\\n        Refunds\\n\\nValidation:\\nProducts can be searched and filtered by category\\n\\nValidation:\\nCart correctly calculates totals including tax and shipping\\n\\nValidation:\\nOrders transition through all status states correctly\\n\\nValidation:\\nPayment is processed and confirmed within 30 seconds\\n\\nValidation:\\nInventory is decremented when order is placed\\n\\nEntity:\\n    Product\\n    id: integer\\n    name: string\\n    description: string\\n    price: float\\n    category: string\\n    stock_quantity: integer\\n    sku: string\\n    active: boolean\\n\\nEntity:\\n    CartItem\\n    product_id: integer\\n    quantity: integer\\n    unit_price: float\\n    added_at: datetime\\n\\nEntity:\\n    Order\\n    id: integer\\n    customer_id: integer\\n    items: list\\n    total: float\\n    status: string\\n    created_at: datetime\\n    shipping_address: dict\\n\\nEntity:\\n    Payment\\n    id: integer\\n    order_id: integer\\n    amount: float\\n    method: string\\n    status: string\\n    transaction_id: string\\n    processed_at: datetime\\n\\nEntity:\\n    Customer\\n    id: integer\\n    email: string\\n    name: string\\n    addresses: list\\n    payment_methods: list\\n\\nBehavior:\\n    SearchProducts\\n    Input: query string, category string\\n    Output: products list\\n    Action: Filter products by name and category matching query\\n\\nBehavior:\\n    AddToCart\\n    Input: cart list, product Product, quantity integer\\n    Output: updated_cart list\\n    Action: Add product to cart with quantity and current price\\n\\nBehavior:\\n    CalculateTotal\\n    Input: items list, tax_rate float, shipping_cost float\\n    Output: total float\\n    Action: Compute subtotal, apply tax rate, add shipping cost\\n\\nBehavior:\\n    PlaceOrder\\n    Input: customer Customer, cart_items list, payment_method string\\n    Output: order Order\\n    Action: Create order from cart items, set status to pending, reserve inventory\\n\\nBehavior:\\n    ProcessPayment\\n    Input: order Order, payment_method string\\n    Output: payment Payment\\n    Action: Submit payment to gateway, record transaction, update order status\\n\\nBehavior:\\n    UpdateInventory\\n    Input: product Product, quantity_sold integer\\n    Output: updated_product Product\\n    Action: Decrement product stock quantity by quantity sold\\n\\nBehavior:\\n    IssueRefund\\n    Input: order Order, reason string\\n    Output: payment Payment\\n    Action: Process refund through payment gateway, restore inventory\\n\\nCondition:\\n    When: product stock_quantity drops below 5\\n    Then: Flag product for reorder and notify purchasing team\\n\\nCondition:\\n    When: order total exceeds 1000\\n    Then: Require additional payment verification\\n\\nCondition:\\n    When: customer has more than 3 pending orders\\n    Then: Queue new order for manual review\\n\\nEvent:\\n    On: ProductAdded\\n    Do: Index product in search engine and update category counts\\n\\nEvent:\\n    On: OrderPlaced\\n    Do: Send confirmation email, update inventory, notify warehouse\\n\\nEvent:\\n    On: PaymentCompleted\\n    Do: Update order status to processing, generate invoice\\n\\nEvent:\\n    On: RefundIssued\\n    Do: Restore inventory, send refund notification, update customer history\\n\\nParallel:\\n    Catalog\\n    Cart\\n\\nOptimize:\\n    Product search latency\\n    Priority: Critical\\n\\nOptimize:\\n    Cart calculation speed\\n    Priority: High\\n\\nOptimize:\\n    Order processing throughput\\n    Priority: High\\n\\nEncrypt:\\n    customer payment_methods\\n    customer addresses\\n\\nProtect:\\n    Payment processing from unauthorized access\\n    Customer data from data breaches\"}\n{\"id\": \"aicl_002\", \"domain\": \"healthcare\", \"levels\": [1,2,3,4,5,6,7,8,9,10], \"description\": \"Build a hospital management system with patient records, appointment scheduling, medical prescriptions, and billing\", \"code\": \"# Hospital Management System\\n# All 10 Levels: Complete healthcare platform\\n\\nGoal:\\nCreate a hospital management system with patient records, appointment scheduling, medical prescriptions, lab results, billing, and regulatory compliance\\n\\nConstraint:\\nMust comply with HIPAA privacy regulations\\n\\nConstraint:\\nPatient data must be encrypted at rest and in transit\\n\\nConstraint:\\nSystem must maintain 99.9% uptime for emergency access\\n\\nConstraint:\\nAll medical decisions must have audit trails\\n\\nRisk:\\nPatient data breach or unauthorized access\\n\\nRecovery:\\nImmediately revoke access, notify compliance officer, and log security incident\\n\\nRisk:\\nMedication interaction or allergic reaction\\n\\nRecovery:\\nAlert prescribing physician and pharmacist, flag patient record\\n\\nRisk:\\nAppointment double-booking\\n\\nRecovery:\\nDetect conflict and offer alternative time slots automatically\\n\\nRisk:\\nLab results lost or mismatched\\n\\nRecovery:\\nCross-reference patient ID and sample ID, flag discrepancy for review\\n\\nRisk:\\nBilling calculation error\\n\\nRecovery:\\nRecalculate from source data and flag for billing department review\\n\\nRisk:\\nSystem unavailable during emergency\\n\\nRecovery:\\nFailover to backup system within 30 seconds and use offline mode\\n\\nLayer:\\n    PatientManagement\\n    SubLayer:\\n        Registration\\n    SubLayer:\\n        Records\\n\\nLayer:\\n    Clinical\\n    SubLayer:\\n        Appointments\\n    SubLayer:\\n        Prescriptions\\n    SubLayer:\\n        LabResults\\n\\nLayer:\\n    Billing\\n    SubLayer:\\n        Insurance\\n    SubLayer:\\n        Invoicing\\n\\nLayer:\\n    Administration\\n    SubLayer:\\n        Staff\\n    SubLayer:\\n        Compliance\\n\\nValidation:\\nPatient records are accessible only by authorized medical staff\\n\\nValidation:\\nAppointments can be scheduled, rescheduled, and cancelled correctly\\n\\nValidation:\\nPrescriptions check for drug interactions before approval\\n\\nValidation:\\nLab results are matched to the correct patient\\n\\nValidation:\\nBilling accurately reflects services rendered\\n\\nValidation:\\nEmergency access is always available\\n\\nEntity:\\n    Patient\\n    id: integer\\n    name: string\\n    date_of_birth: datetime\\n    insurance_id: string\\n    medical_history: list\\n    allergies: list\\n    emergency_contact: dict\\n    blood_type: string\\n\\nEntity:\\n    Doctor\\n    id: integer\\n    name: string\\n    specialty: string\\n    license_number: string\\n    schedule: dict\\n    department: string\\n\\nEntity:\\n    Appointment\\n    id: integer\\n    patient_id: integer\\n    doctor_id: integer\\n    datetime: datetime\\n    duration: integer\\n    status: string\\n    reason: string\\n    notes: string\\n\\nEntity:\\n    Prescription\\n    id: integer\\n    patient_id: integer\\n    doctor_id: integer\\n    medications: list\\n    date_issued: datetime\\n    status: string\\n    pharmacy_id: integer\\n\\nEntity:\\n    LabResult\\n    id: integer\\n    patient_id: integer\\n    test_name: string\\n    result_value: float\\n    unit: string\\n    reference_range: string\\n    status: string\\n    collected_at: datetime\\n\\nEntity:\\n    Invoice\\n    id: integer\\n    patient_id: integer\\n    services: list\\n    total_amount: float\\n    insurance_coverage: float\\n    patient_responsibility: float\\n    status: string\\n    issued_at: datetime\\n\\nEntity:\\n    Department\\n    id: integer\\n    name: string\\n    head_doctor_id: integer\\n    capacity: integer\\n    current_occupancy: integer\\n\\nBehavior:\\n    RegisterPatient\\n    Input: name string, dob datetime, insurance_id string\\n    Output: patient Patient\\n    Action: Create new patient record with demographic and insurance information\\n\\nBehavior:\\n    ScheduleAppointment\\n    Input: patient_id integer, doctor_id integer, preferred_time datetime, reason string\\n    Output: appointment Appointment\\n    Action: Find available slot and create appointment with conflict detection\\n\\nBehavior:\\n    PrescribeMedication\\n    Input: patient_id integer, doctor_id integer, medications list\\n    Output: prescription Prescription\\n    Action: Check drug interactions and allergies, then create prescription\\n\\nBehavior:\\n    RecordLabResult\\n    Input: patient_id integer, test_name string, result_value float, unit string\\n    Output: lab_result LabResult\\n    Action: Store lab result with reference range and flag abnormal values\\n\\nBehavior:\\n    GenerateInvoice\\n    Input: patient_id integer, services list, insurance_id string\\n    Output: invoice Invoice\\n    Action: Calculate costs, apply insurance coverage, generate invoice\\n\\nBehavior:\\n    AdmitPatient\\n    Input: patient_id integer, department_id integer, reason string\\n    Output: admission dict\\n    Action: Assign bed, create admission record, notify department staff\\n\\nBehavior:\\n    DischargePatient\\n    Input: patient_id integer, summary string, follow_up datetime\\n    Output: discharge dict\\n    Action: Generate discharge summary, schedule follow-up, release bed\\n\\nCondition:\\n    When: prescription medications have known interaction\\n    Then: Block prescription and alert prescribing doctor immediately\\n\\nCondition:\\n    When: lab result is outside normal reference range\\n    Then: Flag result as abnormal and notify attending physician\\n\\nCondition:\\n    When: department occupancy reaches 90 percent\\n    Then: Alert administration and activate overflow protocol\\n\\nCondition:\\n    When: patient has outstanding unpaid invoices over 90 days\\n    Then: Flag account for financial counseling\\n\\nEvent:\\n    On: PatientAdmitted\\n    Do: Assign bed, create chart, notify nursing staff, begin monitoring\\n\\nEvent:\\n    On: LabResultReady\\n    Do: Notify requesting doctor, update patient record, flag critical values\\n\\nEvent:\\n    On: PrescriptionFilled\\n    Do: Update medication record, log administration time\\n\\nEvent:\\n    On: PatientDischarged\\n    Do: Release bed, generate discharge summary, schedule follow-up, update billing\\n\\nParallel:\\n    Clinical\\n    Billing\\n\\nOptimize:\\n    Appointment scheduling efficiency\\n    Priority: High\\n\\nOptimize:\\n    Emergency response time\\n    Priority: Critical\\n\\nLearn:\\n    no_show_prediction\\n    Based: appointment history, weather, day_of_week, patient_age\\n    Adapt: overbook_slots by 5 percent when no_show_probability exceeds 30 percent\\n\\nLearn:\\n    readmission_risk\\n    Based: diagnosis, length_of_stay, prior_admissions, social_factors\\n    Adapt: schedule follow_up within 48 hours when risk_score exceeds 70 percent\\n\\nEncrypt:\\n    patient medical_history\\n    patient allergies\\n    prescription medications\\n\\nProtect:\\n    Patient records from unauthorized access\\n    Medical decisions from tampering\\n    Billing data from fraud\\n\\nNative:\\n    hl7_interface\\n    Language: Python\\n    Code: \\\"import hl7; parser = hl7.parse(raw_message); return parser.extract_segments(['PID', 'PV1'])\\\"\"}\n{\"id\": \"aicl_003\", \"domain\": \"social_media\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a social media platform with user profiles, posts, likes, comments, followers, news feed, and content moderation\", \"code\": \"# Social Media Platform\\n# Levels 1-9: Complete social network\\n\\nGoal:\\nCreate a social media platform with user profiles, posts, likes, comments, followers, personalized news feed, and AI-powered content moderation\\n\\nConstraint:\\nContent must be moderated within 5 minutes of posting\\n\\nConstraint:\\nFeed generation must complete within 200ms per user\\n\\nConstraint:\\nSystem must support 1 million concurrent users\\n\\nRisk:\\nInappropriate content posted before moderation\\n\\nRecovery:\\nFlag content for review and temporarily hide from public feed\\n\\nRisk:\\nFeed algorithm creates echo chambers\\n\\nRecovery:\\nInject diverse content and notify user of content diversity score\\n\\nRisk:\\nUser account compromised\\n\\nRecovery:\\nForce password reset, revoke sessions, and notify user via email\\n\\nRisk:\\nViral post overwhelms infrastructure\\n\\nRecovery:\\nEnable rate limiting and serve from cache\\n\\nLayer:\\n    Users\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Authentication\\n\\nLayer:\\n    Content\\n    SubLayer:\\n        Posts\\n    SubLayer:\\n        Comments\\n    SubLayer:\\n        Reactions\\n\\nLayer:\\n    Social\\n    SubLayer:\\n        Followers\\n    SubLayer:\\n        Feed\\n\\nLayer:\\n    Moderation\\n    SubLayer:\\n        Detection\\n    SubLayer:\\n        Review\\n\\nValidation:\\nUsers can create, edit, and delete their own posts\\n\\nValidation:\\nFeed displays relevant content from followed users\\n\\nValidation:\\nContent moderation catches violations within 5 minutes\\n\\nValidation:\\nFollower relationships are correctly maintained\\n\\nEntity:\\n    User\\n    id: integer\\n    username: string\\n    email: string\\n    bio: string\\n    avatar_url: string\\n    is_private: boolean\\n    created_at: datetime\\n    follower_count: integer\\n    following_count: integer\\n\\nEntity:\\n    Post\\n    id: integer\\n    author_id: integer\\n    content: string\\n    media_urls: list\\n    like_count: integer\\n    comment_count: integer\\n    created_at: datetime\\n    visibility: string\\n\\nEntity:\\n    Comment\\n    id: integer\\n    post_id: integer\\n    author_id: integer\\n    content: string\\n    created_at: datetime\\n\\nEntity:\\n    Follow\\n    follower_id: integer\\n    following_id: integer\\n    created_at: datetime\\n    status: string\\n\\nEntity:\\n    Notification\\n    id: integer\\n    user_id: integer\\n    type: string\\n    content: string\\n    read: boolean\\n    created_at: datetime\\n\\nEntity:\\n    ModerationFlag\\n    id: integer\\n    content_id: integer\\n    content_type: string\\n    reason: string\\n    status: string\\n    reviewer_id: integer\\n    created_at: datetime\\n\\nBehavior:\\n    CreatePost\\n    Input: author_id integer, content string, media_urls list, visibility string\\n    Output: post Post\\n    Action: Create post and submit for moderation check\\n\\nBehavior:\\n    LikePost\\n    Input: user_id integer, post_id integer\\n    Output: reaction dict\\n    Action: Record like and update post like count\\n\\nBehavior:\\n    AddComment\\n    Input: post_id integer, author_id integer, content string\\n    Output: comment Comment\\n    Action: Create comment and notify post author\\n\\nBehavior:\\n    FollowUser\\n    Input: follower_id integer, following_id integer\\n    Output: follow Follow\\n    Action: Create follow relationship and generate notification\\n\\nBehavior:\\n    GenerateFeed\\n    Input: user_id integer, page integer\\n    Output: posts list\\n    Action: Aggregate posts from followed users ranked by relevance and recency\\n\\nBehavior:\\n    ModerateContent\\n    Input: content string, content_type string\\n    Output: flag ModerationFlag\\n    Action: Run content through moderation pipeline and flag violations\\n\\nBehavior:\\n    SendNotification\\n    Input: user_id integer, type string, content string\\n    Output: notification Notification\\n    Action: Create notification and push to user devices\\n\\nCondition:\\n    When: user is private and receives follow request\\n    Then: Queue request for user approval instead of auto-following\\n\\nCondition:\\n    When: post receives more than 1000 likes in one hour\\n    Then: Mark as trending and boost visibility\\n\\nCondition:\\n    When: content matches harassment pattern\\n    Then: Auto-hide content and escalate to human reviewer\\n\\nEvent:\\n    On: PostCreated\\n    Do: Add to followers feeds and run moderation check\\n\\nEvent:\\n    On: UserFollowed\\n    Do: Update follower counts and send notification to followed user\\n\\nEvent:\\n    On: ContentFlagged\\n    Do: Hide content from public view and notify content author\\n\\nEvent:\\n    On: TrendingDetected\\n    Do: Boost post distribution and add to trending section\\n\\nParallel:\\n    Content\\n    Moderation\\n\\nOptimize:\\n    Feed generation latency\\n    Priority: Critical\\n\\nOptimize:\\n    Content moderation speed\\n    Priority: High\\n\\nOptimize:\\n    Notification delivery latency\\n    Priority: Medium\\n\\nLearn:\\n    feed_ranking\\n    Based: engagement_history, post_recency, affinity_score, content_type\\n    Adapt: weight factors based on user interaction patterns\\n\\nLearn:\\n    toxicity_detection\\n    Based: text_features, user_history, community_standards\\n    Adapt: threshold based on false positive rate feedback\\n\\nEncrypt:\\n    user email\\n    user authentication_tokens\\n\\nProtect:\\n    User data from unauthorized access\\n    Content from manipulation\\n    Feed algorithm from gaming\"}\n{\"id\": \"aicl_004\", \"domain\": \"iot_smart_home\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build an IoT smart home system with device management, sensor monitoring, automation rules, and energy optimization\", \"code\": \"# IoT Smart Home System\\n# Levels 1-8: Connected home with automation and learning\\n\\nGoal:\\nCreate an IoT smart home system with device management, sensor monitoring, automation rules, energy optimization, and predictive maintenance\\n\\nConstraint:\\nDevice commands must execute within 500ms\\n\\nConstraint:\\nSystem must function offline for critical automations\\n\\nConstraint:\\nSupport up to 200 connected devices per home\\n\\nRisk:\\nDevice fails to respond to command\\n\\nRecovery:\\nRetry command 3 times with exponential backoff, then alert homeowner\\n\\nRisk:\\nSecurity breach through IoT device\\n\\nRecovery:\\nIsolate compromised device and notify security monitoring\\n\\nRisk:\\nAutomation rule conflicts\\n\\nRecovery:\\nDetect conflicts and request user clarification\\n\\nRisk:\\nPower outage disrupts system\\n\\nRecovery:\\nSwitch to battery backup for critical sensors and queue commands\\n\\nLayer:\\n    Devices\\n    SubLayer:\\n        Discovery\\n    SubLayer:\\n        Control\\n\\nLayer:\\n    Sensors\\n    SubLayer:\\n        Monitoring\\n    SubLayer:\\n        Alerts\\n\\nLayer:\\n    Automation\\n    SubLayer:\\n        Rules\\n    SubLayer:\\n        Scenes\\n\\nLayer:\\n    Energy\\n    SubLayer:\\n        Monitoring\\n    SubLayer:\\n        Optimization\\n\\nValidation:\\nDevices can be discovered, paired, and controlled\\n\\nValidation:\\nSensor readings are collected and threshold alerts fire correctly\\n\\nValidation:\\nAutomation rules execute in response to triggers\\n\\nValidation:\\nEnergy usage is tracked and optimized\\n\\nEntity:\\n    Device\\n    id: integer\\n    name: string\\n    type: string\\n    room: string\\n    status: string\\n    firmware_version: string\\n    last_seen: datetime\\n    is_online: boolean\\n\\nEntity:\\n    SensorReading\\n    id: integer\\n    device_id: integer\\n    metric: string\\n    value: float\\n    unit: string\\n    timestamp: datetime\\n\\nEntity:\\n    AutomationRule\\n    id: integer\\n    name: string\\n    trigger_condition: string\\n    actions: list\\n    is_active: boolean\\n    priority: integer\\n\\nEntity:\\n    Scene\\n    id: integer\\n    name: string\\n    device_states: dict\\n    schedule: string\\n\\nEntity:\\n    EnergyUsage\\n    id: integer\\n    device_id: integer\\n    consumption_kwh: float\\n    period: string\\n    timestamp: datetime\\n\\nEntity:\\n    Alert\\n    id: integer\\n    device_id: integer\\n    type: string\\n    message: string\\n    severity: string\\n    acknowledged: boolean\\n    created_at: datetime\\n\\nBehavior:\\n    DiscoverDevice\\n    Input: protocol string, timeout integer\\n    Output: device Device\\n    Action: Scan network for new devices and register them\\n\\nBehavior:\\n    ControlDevice\\n    Input: device_id integer, command string, params dict\\n    Output: result dict\\n    Action: Send command to device and confirm execution\\n\\nBehavior:\\n    RecordReading\\n    Input: device_id integer, metric string, value float, unit string\\n    Output: reading SensorReading\\n    Action: Store sensor reading and check threshold alerts\\n\\nBehavior:\\n    ExecuteAutomation\\n    Input: rule AutomationRule, trigger_data dict\\n    Output: results list\\n    Action: Evaluate rule conditions and execute actions in sequence\\n\\nBehavior:\\n    ActivateScene\\n    Input: scene_id integer\\n    Output: results list\\n    Action: Apply all device states defined in the scene\\n\\nBehavior:\\n    OptimizeEnergy\\n    Input: usage_history list, current_demand float\\n    Output: recommendations list\\n    Action: Analyze consumption patterns and suggest optimizations\\n\\nBehavior:\\n    GenerateAlert\\n    Input: device_id integer, type string, message string, severity string\\n    Output: alert Alert\\n    Action: Create alert and notify homeowner through preferred channel\\n\\nCondition:\\n    When: temperature reading exceeds 30 degrees celsius\\n    Then: Turn on air conditioning and send heat alert notification\\n\\nCondition:\\n    When: motion detected at front door after midnight\\n    Then: Turn on outdoor lights and send security alert\\n\\nCondition:\\n    When: energy consumption exceeds daily budget\\n    Then: Dim non-essential lights and postpone high-consumption automations\\n\\nCondition:\\n    When: device offline for more than 10 minutes\\n    Then: Mark device as disconnected and attempt reconnection\\n\\nEvent:\\n    On: DeviceDiscovered\\n    Do: Add to device registry and prompt user for room assignment\\n\\nEvent:\\n    On: ThresholdBreached\\n    Do: Generate alert and check automation rules for matching triggers\\n\\nEvent:\\n    On: SceneActivated\\n    Do: Log scene activation and update device states in UI\\n\\nEvent:\\n    On: EnergyAnomalyDetected\\n    Do: Generate efficiency report and suggest corrective actions\\n\\nParallel:\\n    Sensors\\n    Automation\\n\\nOptimize:\\n    Device command latency\\n    Priority: Critical\\n\\nOptimize:\\n    Energy consumption efficiency\\n    Priority: High\\n\\nLearn:\\n    occupancy_pattern\\n    Based: motion_sensors, time_of_day, day_of_week, historical_presence\\n    Adapt: pre_heat_or_cool home 30 minutes before expected arrival\\n\\nLearn:\\n    energy_forecast\\n    Based: weather_forecast, historical_usage, occupancy_prediction\\n    Adapt: adjust thermostat and schedule high-consumption devices off-peak\"}\n{\"id\": \"aicl_005\", \"domain\": \"ride_sharing\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a ride-sharing platform with driver management, trip matching, dynamic pricing, and safety features\", \"code\": \"# Ride-Sharing Platform\\n# Levels 1-9: Complete ride-hailing with dynamic pricing and safety\\n\\nGoal:\\nCreate a ride-sharing platform with driver onboarding, real-time trip matching, dynamic pricing, GPS tracking, rating system, and safety features\\n\\nConstraint:\\nDriver matching must complete within 5 seconds\\n\\nConstraint:\\nGPS location updates every 3 seconds\\n\\nConstraint:\\nAll rides must have recording capability for dispute resolution\\n\\nRisk:\\nNo drivers available in high-demand area\\n\\nRecovery:\\nActivate surge pricing to attract drivers and notify waiting riders of estimated wait\\n\\nRisk:\\nDriver or rider safety incident\\n\\nRecovery:\\nTrigger emergency protocol, share live location with contacts, and contact authorities\\n\\nRisk:\\nGPS signal lost during trip\\n\\nRecovery:\\nSwitch to cell tower triangulation and estimate route from last known position\\n\\nRisk:\\nPayment dispute after trip\\n\\nRecovery:\\nHold payment in escrow and open dispute resolution process\\n\\nLayer:\\n    Riders\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Requests\\n\\nLayer:\\n    Drivers\\n    SubLayer:\\n        Onboarding\\n    SubLayer:\\n        Dispatch\\n\\nLayer:\\n    Trips\\n    SubLayer:\\n        Matching\\n    SubLayer:\\n        Tracking\\n\\nLayer:\\n    Pricing\\n    SubLayer:\\n        Calculation\\n    SubLayer:\\n        Settlement\\n\\nLayer:\\n    Safety\\n    SubLayer:\\n        Monitoring\\n    SubLayer:\\n        Emergency\\n\\nValidation:\\nRiders can request and cancel rides\\n\\nValidation:\\nDrivers are matched to riders within 5 seconds\\n\\nValidation:\\nDynamic pricing reflects supply and demand\\n\\nValidation:\\nSafety features work during active trips\\n\\nValidation:\\nPayments are settled correctly after trip completion\\n\\nEntity:\\n    Rider\\n    id: integer\\n    name: string\\n    phone: string\\n    email: string\\n    rating: float\\n    total_trips: integer\\n    payment_method: string\\n\\nEntity:\\n    Driver\\n    id: integer\\n    name: string\\n    phone: string\\n    vehicle_info: dict\\n    license_number: string\\n    rating: float\\n    total_trips: integer\\n    is_available: boolean\\n    current_location: dict\\n\\nEntity:\\n    Trip\\n    id: integer\\n    rider_id: integer\\n    driver_id: integer\\n    pickup_location: dict\\n    dropoff_location: dict\\n    status: string\\n    fare: float\\n    distance: float\\n    duration: integer\\n    started_at: datetime\\n    completed_at: datetime\\n\\nEntity:\\n    Fare\\n    id: integer\\n    trip_id: integer\\n    base_fare: float\\n    distance_fare: float\\n    time_fare: float\\n    surge_multiplier: float\\n    total: float\\n    currency: string\\n\\nEntity:\\n    Rating\\n    id: integer\\n    trip_id: integer\\n    reviewer_id: integer\\n    reviewee_id: integer\\n    score: integer\\n    comment: string\\n    created_at: datetime\\n\\nEntity:\\n    SafetyAlert\\n    id: integer\\n    trip_id: integer\\n    type: string\\n    description: string\\n    severity: string\\n    resolved: boolean\\n    created_at: datetime\\n\\nBehavior:\\n    RequestRide\\n    Input: rider_id integer, pickup dict, dropoff dict, ride_type string\\n    Output: trip Trip\\n    Action: Create ride request and search for available drivers\\n\\nBehavior:\\n    MatchDriver\\n    Input: trip Trip, available_drivers list\\n    Output: matched_driver Driver\\n    Action: Find nearest available driver with best rating and ETA\\n\\nBehavior:\\n    CalculateFare\\n    Input: distance float, duration integer, surge_multiplier float, ride_type string\\n    Output: fare Fare\\n    Action: Compute base, distance, time, and surge components\\n\\nBehavior:\\n    TrackTrip\\n    Input: trip_id integer, driver_location dict\\n    Output: updated_trip Trip\\n    Action: Update trip route and ETA based on live GPS data\\n\\nBehavior:\\n    CompleteTrip\\n    Input: trip_id integer, final_location dict\\n    Output: completed_trip Trip\\n    Action: Finalize fare, trigger payment, and request ratings\\n\\nBehavior:\\n    RateParticipant\\n    Input: trip_id integer, reviewer_id integer, reviewee_id integer, score integer, comment string\\n    Output: rating Rating\\n    Action: Record rating and update participant average\\n\\nBehavior:\\n    TriggerEmergency\\n    Input: trip_id integer, user_id integer, alert_type string\\n    Output: alert SafetyAlert\\n    Action: Contact authorities, share location, and activate safety protocol\\n\\nCondition:\\n    When: demand exceeds available drivers by 50 percent\\n    Then: Activate surge pricing and notify riders of increased fares\\n\\nCondition:\\n    When: driver deviates from route by more than 500 meters\\n    Then: Log deviation and check for safety alert trigger\\n\\nCondition:\\n    When: trip duration exceeds estimated time by 40 percent\\n    Then: Notify rider of delay and check for traffic issues\\n\\nEvent:\\n    On: RideRequested\\n    Do: Broadcast to nearby drivers and start 30-second matching window\\n\\nEvent:\\n    On: DriverAccepted\\n    Do: Notify rider with driver info and ETA, start tracking\\n\\nEvent:\\n    On: TripCompleted\\n    Do: Process payment, request mutual ratings, update driver availability\\n\\nEvent:\\n    On: SafetyAlertTriggered\\n    Do: Contact emergency services, share live location with trusted contacts\\n\\nParallel:\\n    Trips\\n    Pricing\\n\\nOptimize:\\n    Driver matching speed\\n    Priority: Critical\\n\\nOptimize:\\n    Fare calculation accuracy\\n    Priority: High\\n\\nLearn:\\n    demand_prediction\\n    Based: time_of_day, weather, events, historical_demand\\n    Adapt: pre_position drivers in high-demand areas before predicted surge\\n\\nLearn:\\n    eta_estimation\\n    Based: route_data, traffic_patterns, weather_conditions, time_of_day\\n    Adapt: adjust eta model based on actual vs predicted arrival times\\n\\nEncrypt:\\n    rider phone\\n    rider email\\n    driver phone\\n\\nProtect:\\n    Trip tracking data from unauthorized access\\n    Payment information from fraud\\n    Driver location history from stalking\"}\n{\"id\": \"aicl_006\", \"domain\": \"online_education\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build an online education platform with courses, students, assignments, grading, and adaptive learning\", \"code\": \"# Online Education Platform\\n# Levels 1-8: Learning management with adaptive features\\n\\nGoal:\\nCreate an online education platform with course management, student enrollment, assignment submission, grading, progress tracking, and adaptive learning paths\\n\\nConstraint:\\nVideo streaming must support at least 10000 concurrent viewers\\n\\nConstraint:\\nAssignment submission deadline must be enforced to the second\\n\\nRisk:\\nStudent loses work during assignment submission\\n\\nRecovery:\\nAuto-save draft every 30 seconds and restore from last save on reconnect\\n\\nRisk:\\nPlagiarism detected in submitted work\\n\\nRecovery:\\nFlag submission for academic review and notify student of policy violation\\n\\nRisk:\\nCourse content inaccessible during exam\\n\\nRecovery:\\nSwitch to backup content server and extend exam deadline by lost time\\n\\nLayer:\\n    Courses\\n    SubLayer:\\n        Content\\n    SubLayer:\\n        Schedule\\n\\nLayer:\\n    Students\\n    SubLayer:\\n        Enrollment\\n    SubLayer:\\n        Progress\\n\\nLayer:\\n    Assessment\\n    SubLayer:\\n        Assignments\\n    SubLayer:\\n        Grading\\n\\nValidation:\\nStudents can enroll in and drop courses within allowed periods\\n\\nValidation:\\nAssignments can be submitted and graded correctly\\n\\nValidation:\\nProgress tracking accurately reflects completion\\n\\nEntity:\\n    Course\\n    id: integer\\n    title: string\\n    description: string\\n    instructor_id: integer\\n    start_date: datetime\\n    end_date: datetime\\n    max_enrollment: integer\\n    syllabus: list\\n    difficulty: string\\n\\nEntity:\\n    Student\\n    id: integer\\n    name: string\\n    email: string\\n    enrolled_courses: list\\n    gpa: float\\n    completed_courses: list\\n\\nEntity:\\n    Assignment\\n    id: integer\\n    course_id: integer\\n    title: string\\n    description: string\\n    due_date: datetime\\n    max_points: float\\n    type: string\\n\\nEntity:\\n    Submission\\n    id: integer\\n    assignment_id: integer\\n    student_id: integer\\n    content: string\\n    submitted_at: datetime\\n    grade: float\\n    feedback: string\\n    status: string\\n\\nEntity:\\n    Progress\\n    id: integer\\n    student_id: integer\\n    course_id: integer\\n    completion_percent: float\\n    last_accessed: datetime\\n    time_spent: integer\\n\\nBehavior:\\n    EnrollStudent\\n    Input: student_id integer, course_id integer\\n    Output: enrollment dict\\n    Action: Register student in course if capacity allows and prerequisites met\\n\\nBehavior:\\n    SubmitAssignment\\n    Input: assignment_id integer, student_id integer, content string\\n    Output: submission Submission\\n    Action: Record submission with timestamp and check against deadline\\n\\nBehavior:\\n    GradeSubmission\\n    Input: submission_id integer, grade float, feedback string\\n    Output: graded_submission Submission\\n    Action: Apply grade and feedback, update student progress\\n\\nBehavior:\\n    TrackProgress\\n    Input: student_id integer, course_id integer\\n    Output: progress Progress\\n    Action: Calculate completion percentage from submitted and graded work\\n\\nBehavior:\\n    GenerateReport\\n    Input: student_id integer, course_id integer\\n    Output: report dict\\n    Action: Compile grades, progress, and time spent into student report\\n\\nBehavior:\\n    CheckPrerequisites\\n    Input: student_id integer, course_id integer\\n    Output: eligible boolean\\n    Action: Verify student has completed all prerequisite courses\\n\\nCondition:\\n    When: assignment submitted after deadline\\n    Then: Mark as late and apply penalty according to course policy\\n\\nCondition:\\n    When: student completion_percent drops below 30 percent at midpoint\\n    Then: Flag student for intervention and notify academic advisor\\n\\nCondition:\\n    When: course enrollment exceeds 90 percent capacity\\n    Then: Create waitlist and notify prospective students\\n\\nEvent:\\n    On: StudentEnrolled\\n    Do: Send welcome email, create progress tracker, add to course roster\\n\\nEvent:\\n    On: AssignmentGraded\\n    Do: Update student progress, send grade notification, check completion criteria\\n\\nEvent:\\n    On: CourseCompleted\\n    Do: Issue certificate, update transcript, request course evaluation\\n\\nParallel:\\n    Assessment\\n    Students\\n\\nOptimize:\\n    Video streaming quality\\n    Priority: High\\n\\nOptimize:\\n    Assignment grading turnaround\\n    Priority: Medium\\n\\nLearn:\\n    difficulty_adaptation\\n    Based: student_performance, time_per_assignment, quiz_scores, engagement_metrics\\n    Adapt: recommend supplementary content when struggling and advance when excelling\"}\n{\"id\": \"aicl_007\", \"domain\": \"inventory_management\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build an inventory management system with warehouses, products, shipments, suppliers, and stock tracking\", \"code\": \"# Inventory Management System\\n# Levels 1-7: Warehouse and supply chain tracking\\n\\nGoal:\\nCreate an inventory management system with multi-warehouse tracking, product catalog, shipment management, supplier coordination, and automated reorder\\n\\nConstraint:\\nStock levels must update in real-time across all warehouses\\n\\nConstraint:\\nReorder alerts must trigger before stock reaches critical levels\\n\\nRisk:\\nStockout on critical products\\n\\nRecovery:\\nEmergency order from backup supplier and expedite shipping\\n\\nRisk:\\nWarehouse inventory count discrepancy\\n\\nRecovery:\\nFreeze stock movement and initiate cycle count audit\\n\\nRisk:\\nSupplier delivery delayed\\n\\nRecovery:\\nActivate alternative supplier and adjust production schedule\\n\\nLayer:\\n    Products\\n    SubLayer:\\n        Catalog\\n    SubLayer:\\n        Categories\\n\\nLayer:\\n    Warehouses\\n    SubLayer:\\n        Locations\\n    SubLayer:\\n        Stock\\n\\nLayer:\\n    Supply\\n    SubLayer:\\n        Suppliers\\n    SubLayer:\\n        Orders\\n\\nLayer:\\n    Shipping\\n    SubLayer:\\n        Outbound\\n    SubLayer:\\n        Receiving\\n\\nValidation:\\nProduct stock levels are accurate across all warehouses\\n\\nValidation:\\nReorder points trigger purchase orders automatically\\n\\nValidation:\\nShipments are tracked from dispatch to receipt\\n\\nEntity:\\n    Product\\n    id: integer\\n    name: string\\n    sku: string\\n    category: string\\n    unit_price: float\\n    reorder_point: integer\\n    reorder_quantity: integer\\n    unit: string\\n\\nEntity:\\n    Warehouse\\n    id: integer\\n    name: string\\n    location: string\\n    capacity: integer\\n    manager_id: integer\\n\\nEntity:\\n    StockLevel\\n    product_id: integer\\n    warehouse_id: integer\\n    quantity: integer\\n    reserved_quantity: integer\\n    last_counted: datetime\\n\\nEntity:\\n    Supplier\\n    id: integer\\n    name: string\\n    contact_email: string\\n    lead_time_days: integer\\n    rating: float\\n    products_supplied: list\\n\\nEntity:\\n    PurchaseOrder\\n    id: integer\\n    supplier_id: integer\\n    products: list\\n    total_amount: float\\n    status: string\\n    order_date: datetime\\n    expected_delivery: datetime\\n\\nEntity:\\n    Shipment\\n    id: integer\\n    origin_id: integer\\n    destination_id: integer\\n    items: list\\n    status: string\\n    tracking_number: string\\n    shipped_at: datetime\\n    delivered_at: datetime\\n\\nBehavior:\\n    AddProduct\\n    Input: name string, sku string, category string, price float\\n    Output: product Product\\n    Action: Create product in catalog with reorder parameters\\n\\nBehavior:\\n    UpdateStock\\n    Input: product_id integer, warehouse_id integer, quantity_change integer\\n    Output: stock StockLevel\\n    Action: Adjust stock level and check reorder threshold\\n\\nBehavior:\\n    CreatePurchaseOrder\\n    Input: supplier_id integer, products list\\n    Output: order PurchaseOrder\\n    Action: Generate purchase order and send to supplier\\n\\nBehavior:\\n    ReceiveShipment\\n    Input: shipment_id integer, received_items list\\n    Output: updated_stock list\\n    Action: Verify received quantities and update stock levels\\n\\nBehavior:\\n    TransferStock\\n    Input: product_id integer, from_warehouse integer, to_warehouse integer, quantity integer\\n    Output: shipment Shipment\\n    Action: Create internal transfer shipment between warehouses\\n\\nBehavior:\\n    CheckReorder\\n    Input: product_id integer, warehouse_id integer\\n    Output: needs_reorder boolean\\n    Action: Compare current stock to reorder point and trigger if needed\\n\\nCondition:\\n    When: stock level falls below reorder point\\n    Then: Automatically generate purchase order to primary supplier\\n\\nCondition:\\n    When: received shipment quantity does not match expected\\n    Then: Log discrepancy and notify warehouse manager for investigation\\n\\nCondition:\\n    When: warehouse capacity exceeds 95 percent\\n    Then: Alert management and halt incoming shipments\\n\\nEvent:\\n    On: StockUpdated\\n    Do: Refresh inventory dashboard and check reorder thresholds\\n\\nEvent:\\n    On: PurchaseOrderApproved\\n    Do: Send order to supplier and schedule expected delivery\\n\\nEvent:\\n    On: ShipmentDelivered\\n    Do: Update stock levels and close shipment record\\n\\nParallel:\\n    Products\\n    Shipping\\n\\nOptimize:\\n    Warehouse space utilization\\n    Priority: High\\n\\nOptimize:\\n    Reorder timing accuracy\\n    Priority: High\"}\n{\"id\": \"aicl_008\", \"domain\": \"flight_booking\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a flight booking system with flight search, reservation, check-in, boarding, and loyalty program\", \"code\": \"# Flight Booking System\\n# Levels 1-9: Airline reservation with loyalty and security\\n\\nGoal:\\nCreate a flight booking system with flight search, seat reservation, passenger check-in, boarding management, loyalty program, and aviation security compliance\\n\\nConstraint:\\nSearch results must return within 2 seconds\\n\\nConstraint:\\nMust comply with TSA security requirements\\n\\nConstraint:\\nOverbooking limited to 10 percent of capacity\\n\\nRisk:\\nFlight overbooking leads to denied boarding\\n\\nRecovery:\\nOffer compensation and rebook on next available flight\\n\\nRisk:\\nPassenger no-show on connecting flight\\n\\nRecovery:\\nAutomatically rebook on next flight and send notification\\n\\nRisk:\\nFlight cancellation due to weather\\n\\nRecovery:\\nRebook all passengers and provide accommodation vouchers\\n\\nRisk:\\nPayment processing failure during booking\\n\\nRecovery:\\nHold reservation for 30 minutes and retry payment\\n\\nLayer:\\n    Flights\\n    SubLayer:\\n        Schedule\\n    SubLayer:\\n        Availability\\n\\nLayer:\\n    Booking\\n    SubLayer:\\n        Search\\n    SubLayer:\\n        Reservation\\n\\nLayer:\\n    CheckIn\\n    SubLayer:\\n        Online\\n    SubLayer:\\n        Airport\\n\\nLayer:\\n    Loyalty\\n    SubLayer:\\n        Points\\n    SubLayer:\\n        Tiers\\n\\nValidation:\\nFlights can be searched by route, date, and passenger count\\n\\nValidation:\\nBookings are confirmed and tickets issued correctly\\n\\nValidation:\\nCheck-in process assigns seats and issues boarding passes\\n\\nValidation:\\nLoyalty points are earned and redeemed correctly\\n\\nEntity:\\n    Flight\\n    id: integer\\n    flight_number: string\\n    origin: string\\n    destination: string\\n    departure_time: datetime\\n    arrival_time: datetime\\n    aircraft_type: string\\n    total_seats: integer\\n    available_seats: integer\\n    base_price: float\\n    status: string\\n\\nEntity:\\n    Passenger\\n    id: integer\\n    name: string\\n    passport_number: string\\n    nationality: string\\n    date_of_birth: datetime\\n    loyalty_id: string\\n    special_needs: list\\n\\nEntity:\\n    Reservation\\n    id: integer\\n    flight_id: integer\\n    passenger_id: integer\\n    seat_number: string\\n    class: string\\n    price_paid: float\\n    status: string\\n    booked_at: datetime\\n\\nEntity:\\n    BoardingPass\\n    id: integer\\n    reservation_id: integer\\n    gate: string\\n    boarding_time: datetime\\n    zone: string\\n    issued_at: datetime\\n\\nEntity:\\n    LoyaltyAccount\\n    id: integer\\n    passenger_id: integer\\n    tier: string\\n    points_balance: integer\\n    lifetime_points: integer\\n\\nBehavior:\\n    SearchFlights\\n    Input: origin string, destination string, date datetime, passengers integer\\n    Output: flights list\\n    Action: Find matching flights with available seats and pricing\\n\\nBehavior:\\n    BookFlight\\n    Input: flight_id integer, passenger_id integer, seat_class string\\n    Output: reservation Reservation\\n    Action: Reserve seat, process payment, and issue confirmation\\n\\nBehavior:\\n    CheckIn\\n    Input: reservation_id integer, bags integer\\n    Output: boarding_pass BoardingPass\\n    Action: Confirm passenger identity, assign seat, and issue boarding pass\\n\\nBehavior:\\n    EarnPoints\\n    Input: passenger_id integer, flight_distance integer, class string\\n    Output: updated_account LoyaltyAccount\\n    Action: Calculate and add loyalty points based on distance and class\\n\\nBehavior:\\n    RedeemPoints\\n    Input: account_id integer, points integer, reward_type string\\n    Output: redemption dict\\n    Action: Deduct points and issue reward or upgrade\\n\\nBehavior:\\n    CancelReservation\\n    Input: reservation_id integer, reason string\\n    Output: refund dict\\n    Action: Release seat, process refund according to fare rules\\n\\nCondition:\\n    When: flight is over 80 percent full\\n    Then: Increase ticket prices by surge factor\\n\\nCondition:\\n    When: passenger has not checked in 45 minutes before departure\\n    Then: Release seat to standby list and notify passenger\\n\\nCondition:\\n    When: loyalty member reaches tier threshold\\n    Then: Automatically upgrade tier and send congratulations notification\\n\\nEvent:\\n    On: FlightBooked\\n    Do: Send confirmation email and add to passenger itinerary\\n\\nEvent:\\n    On: CheckInCompleted\\n    Do: Update flight manifest and assign boarding zone\\n\\nEvent:\\n    On: FlightCancelled\\n    Do: Notify all passengers, process refunds, and offer rebooking\\n\\nEvent:\\n    On: BoardingStarted\\n    Do: Call zones sequentially and track boarding progress\\n\\nParallel:\\n    Booking\\n    Loyalty\\n\\nOptimize:\\n    Flight search speed\\n    Priority: Critical\\n\\nOptimize:\\n    Seat allocation revenue\\n    Priority: High\\n\\nLearn:\\n    demand_forecast\\n    Based: route_history, seasonality, events, competition_pricing\\n    Adapt: adjust pricing and capacity allocation based on predicted demand\\n\\nEncrypt:\\n    passenger passport_number\\n    passenger date_of_birth\\n\\nProtect:\\n    Passenger identity from unauthorized access\\n    Booking data from manipulation\\n    Payment from fraud\"}\n{\"id\": \"aicl_009\", \"domain\": \"music_streaming\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a music streaming service with artists, albums, playlists, tracks, subscriptions, and recommendation engine\", \"code\": \"# Music Streaming Service\\n# Levels 1-8: Audio platform with ML recommendations\\n\\nGoal:\\nCreate a music streaming service with artist management, album catalog, playlist creation, track playback, subscription billing, and AI-powered recommendations\\n\\nConstraint:\\nTrack playback must start within 2 seconds\\n\\nConstraint:\\nRecommendation engine must update within 1 hour of listening activity\\n\\nRisk:\\nMusic licensing expires mid-stream\\n\\nRecovery:\\nRemove track from catalog, notify users in active playlists, and substitute similar track\\n\\nRisk:\\nSubscription payment fails\\n\\nRecovery:\\nRetry payment and provide 7-day grace period before downgrading\\n\\nRisk:\\nRecommendation algorithm creates filter bubble\\n\\nRecovery:\\nInject 15 percent discovery tracks outside user comfort zone\\n\\nLayer:\\n    Catalog\\n    SubLayer:\\n        Artists\\n    SubLayer:\\n        Albums\\n    SubLayer:\\n        Tracks\\n\\nLayer:\\n    Playback\\n    SubLayer:\\n        Streaming\\n    SubLayer:\\n        Queue\\n\\nLayer:\\n    Users\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Playlists\\n\\nLayer:\\n    Subscriptions\\n    SubLayer:\\n        Plans\\n    SubLayer:\\n        Billing\\n\\nValidation:\\nTracks stream without interruption\\n\\nValidation:\\nPlaylists can be created, modified, and shared\\n\\nValidation:\\nRecommendations reflect listening history\\n\\nValidation:\\nSubscription billing is accurate\\n\\nEntity:\\n    Artist\\n    id: integer\\n    name: string\\n    genre: string\\n    bio: string\\n    monthly_listeners: integer\\n    verified: boolean\\n\\nEntity:\\n    Album\\n    id: integer\\n    artist_id: integer\\n    title: string\\n    release_date: datetime\\n    genre: string\\n    track_count: integer\\n    cover_url: string\\n\\nEntity:\\n    Track\\n    id: integer\\n    album_id: integer\\n    title: string\\n    duration: integer\\n    play_count: integer\\n    file_url: string\\n    explicit: boolean\\n\\nEntity:\\n    Playlist\\n    id: integer\\n    user_id: integer\\n    name: string\\n    tracks: list\\n    is_public: boolean\\n    created_at: datetime\\n\\nEntity:\\n    User\\n    id: integer\\n    username: string\\n    email: string\\n    subscription_tier: string\\n    listening_history: list\\n    created_at: datetime\\n\\nEntity:\\n    Subscription\\n    id: integer\\n    user_id: integer\\n    plan: string\\n    price: float\\n    billing_date: datetime\\n    status: string\\n\\nBehavior:\\n    StreamTrack\\n    Input: track_id integer, user_id integer, quality string\\n    Output: stream_url string\\n    Action: Authenticate user and return streaming URL with quality selection\\n\\nBehavior:\\n    CreatePlaylist\\n    Input: user_id integer, name string, tracks list, is_public boolean\\n    Output: playlist Playlist\\n    Action: Create playlist with initial tracks and visibility setting\\n\\nBehavior:\\n    AddToPlaylist\\n    Input: playlist_id integer, track_id integer, position integer\\n    Output: updated_playlist Playlist\\n    Action: Insert track at position and reorder if needed\\n\\nBehavior:\\n    GenerateRecommendations\\n    Input: user_id integer, count integer\\n    Output: tracks list\\n    Action: Analyze listening history and return personalized track suggestions\\n\\nBehavior:\\n    ProcessSubscription\\n    Input: user_id integer, plan string, payment_method string\\n    Output: subscription Subscription\\n    Action: Create or upgrade subscription and process initial payment\\n\\nBehavior:\\n    RecordPlay\\n    Input: user_id integer, track_id integer, duration_played integer\\n    Output: play_record dict\\n    Action: Log play event for analytics and recommendation training\\n\\nCondition:\\n    When: user is on free tier and skips more than 6 tracks per hour\\n    Then: Display audio ad before next track plays\\n\\nCondition:\\n    When: track is marked explicit and user profile is under 18\\n    Then: Block playback and suggest clean version\\n\\nCondition:\\n    When: subscription payment fails\\n    Then: Enter grace period and restrict premium features after 7 days\\n\\nEvent:\\n    On: TrackPlayed\\n    Do: Update play count, refresh recommendations, and record analytics\\n\\nEvent:\\n    On: PlaylistCreated\\n    Do: Index for search and notify followers if public\\n\\nEvent:\\n    On: SubscriptionRenewed\\n    Do: Extend access and send confirmation\\n\\nEvent:\\n    On: ArtistVerified\\n    Do: Update profile badge and boost in search results\\n\\nParallel:\\n    Playback\\n    Catalog\\n\\nOptimize:\\n    Track streaming latency\\n    Priority: Critical\\n\\nOptimize:\\n    Recommendation relevance\\n    Priority: High\\n\\nLearn:\\n    track_recommendation\\n    Based: listening_history, liked_tracks, playlist_additions, genre_affinity\\n    Adapt: weight recommendation factors based on skip rate and save rate\\n\\nLearn:\\n    mood_detection\\n    Based: listening_patterns, time_of_day, track_features\\n    Adapt: suggest playlists matching detected mood context\"}\n{\"id\": \"aicl_010\", \"domain\": \"project_management\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a project management tool with tasks, teams, sprints, deadlines, and workload balancing\", \"code\": \"# Project Management Tool\\n# Levels 1-8: Agile project tracking with adaptive workload\\n\\nGoal:\\nCreate a project management tool with task boards, team management, sprint planning, deadline tracking, and adaptive workload balancing\\n\\nConstraint:\\nTask board must update in real-time across all team members\\n\\nConstraint:\\nSprint velocity calculations must be accurate\\n\\nRisk:\\nSprint scope exceeds team capacity\\n\\nRecovery:\\nAlert scrum master and suggest scope reduction or timeline extension\\n\\nRisk:\\nCritical path task blocked by dependency\\n\\nRecovery:\\nEscalate to project lead and reassign or parallelize dependency\\n\\nRisk:\\nTeam member overloaded with tasks\\n\\nRecovery:\\nRedistribute tasks to available team members with matching skills\\n\\nLayer:\\n    Projects\\n    SubLayer:\\n        Boards\\n    SubLayer:\\n        Timelines\\n\\nLayer:\\n    Tasks\\n    SubLayer:\\n        Items\\n    SubLayer:\\n        Dependencies\\n\\nLayer:\\n    Teams\\n    SubLayer:\\n        Members\\n    SubLayer:\\n        Workload\\n\\nLayer:\\n    Sprints\\n    SubLayer:\\n        Planning\\n    SubLayer:\\n        Velocity\\n\\nValidation:\\nTasks can be created, assigned, and moved through statuses\\n\\nValidation:\\nSprint planning respects team capacity\\n\\nValidation:\\nDependencies are tracked and blockers identified\\n\\nEntity:\\n    Project\\n    id: integer\\n    name: string\\n    description: string\\n    owner_id: integer\\n    status: string\\n    start_date: datetime\\n    deadline: datetime\\n\\nEntity:\\n    Task\\n    id: integer\\n    project_id: integer\\n    title: string\\n    description: string\\n    assignee_id: integer\\n    status: string\\n    priority: string\\n    story_points: integer\\n    due_date: datetime\\n    labels: list\\n\\nEntity:\\n    TeamMember\\n    id: integer\\n    name: string\\n    role: string\\n    skills: list\\n    availability: float\\n    current_load: float\\n\\nEntity:\\n    Sprint\\n    id: integer\\n    project_id: integer\\n    name: string\\n    start_date: datetime\\n    end_date: datetime\\n    velocity: integer\\n    capacity: integer\\n\\nEntity:\\n    Dependency\\n    blocker_task_id: integer\\n    blocked_task_id: integer\\n    type: string\\n\\nBehavior:\\n    CreateTask\\n    Input: project_id integer, title string, description string, priority string\\n    Output: task Task\\n    Action: Create task in backlog with default status and no assignee\\n\\nBehavior:\\n    AssignTask\\n    Input: task_id integer, member_id integer\\n    Output: updated_task Task\\n    Action: Assign task to team member and update workload\\n\\nBehavior:\\n    MoveTask\\n    Input: task_id integer, new_status string\\n    Output: updated_task Task\\n    Action: Move task to new status column and log transition\\n\\nBehavior:\\n    PlanSprint\\n    Input: project_id integer, tasks list, team_capacity integer\\n    Output: sprint Sprint\\n    Action: Validate capacity and create sprint with selected tasks\\n\\nBehavior:\\n    CalculateVelocity\\n    Input: sprint_id integer\\n    Output: velocity integer\\n    Action: Sum completed story points from finished sprint\\n\\nBehavior:\\n    BalanceWorkload\\n    Input: team_members list, tasks list\\n    Output: assignments dict\\n    Action: Redistribute tasks based on availability and skill matching\\n\\nCondition:\\n    When: task due date is within 24 hours and status is not complete\\n    Then: Escalate to project lead and flag as at-risk\\n\\nCondition:\\n    When: sprint completion rate falls below 60 percent at midpoint\\n    Then: Alert scrum master and suggest scope adjustment\\n\\nCondition:\\n    When: team member workload exceeds 120 percent capacity\\n    Then: Flag overload and suggest task redistribution\\n\\nEvent:\\n    On: TaskCompleted\\n    Do: Update sprint burndown, notify dependent tasks, and check sprint goals\\n\\nEvent:\\n    On: SprintStarted\\n    Do: Lock sprint scope and begin velocity tracking\\n\\nEvent:\\n    On: DependencyResolved\\n    Do: Unblock dependent tasks and notify assignees\\n\\nEvent:\\n    On: DeadlineApproaching\\n    Do: Send reminders to assignees and alert project lead\\n\\nParallel:\\n    Tasks\\n    Teams\\n\\nOptimize:\\n    Task board refresh speed\\n    Priority: High\\n\\nOptimize:\\n    Sprint planning efficiency\\n    Priority: Medium\\n\\nLearn:\\n    velocity_prediction\\n    Based: historical_velocity, team_composition, task_complexity, sprint_length\\n    Adapt: adjust sprint capacity estimates based on actual vs predicted velocity\\n\\nLearn:\\n    task_duration_estimate\\n    Based: task_type, assignee_history, complexity_indicators, dependencies_count\\n    Adapt: refine story point estimates based on actual completion times\"}\n{\"id\": \"aicl_011\", \"domain\": \"restaurant_delivery\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a restaurant delivery platform with restaurant management, orders, drivers, menus, and real-time tracking\", \"code\": \"# Restaurant Delivery Platform\\n# Levels 1-7: Food delivery with real-time tracking\\n\\nGoal:\\nCreate a restaurant delivery platform with restaurant management, menu browsing, order placement, driver dispatch, and real-time delivery tracking\\n\\nConstraint:\\nOrder to delivery must complete within 45 minutes\\n\\nConstraint:\\nDriver matching must complete within 30 seconds\\n\\nRisk:\\nRestaurant unable to fulfill order items\\n\\nRecovery:\\nNotify customer of unavailable items and offer substitutes or refund\\n\\nRisk:\\nDelivery driver stuck in traffic\\n\\nRecovery:\\nRecalculate ETA and notify customer with updated estimate\\n\\nRisk:\\nFood arrives cold or damaged\\n\\nRecovery:\\nIssue refund or redelivery and flag restaurant for quality review\\n\\nLayer:\\n    Restaurants\\n    SubLayer:\\n        Menu\\n    SubLayer:\\n        Kitchen\\n\\nLayer:\\n    Orders\\n    SubLayer:\\n        Placement\\n    SubLayer:\\n    Tracking\\n\\nLayer:\\n    Delivery\\n    SubLayer:\\n        Dispatch\\n    SubLayer:\\n        Routing\\n\\nValidation:\\nCustomers can browse menus and place orders\\n\\nValidation:\\nOrders are dispatched to nearest available driver\\n\\nValidation:\\nDelivery progress is tracked in real-time\\n\\nEntity:\\n    Restaurant\\n    id: integer\\n    name: string\\n    cuisine: string\\n    address: string\\n    rating: float\\n    delivery_radius: integer\\n    is_open: boolean\\n    prep_time_minutes: integer\\n\\nEntity:\\n    MenuItem\\n    id: integer\\n    restaurant_id: integer\\n    name: string\\n    description: string\\n    price: float\\n    category: string\\n    available: boolean\\n    allergens: list\\n\\nEntity:\\n    Order\\n    id: integer\\n    customer_id: integer\\n    restaurant_id: integer\\n    items: list\\n    total: float\\n    status: string\\n    placed_at: datetime\\n    estimated_delivery: datetime\\n\\nEntity:\\n    Driver\\n    id: integer\\n    name: string\\n    phone: string\\n    vehicle_type: string\\n    is_available: boolean\\n    current_location: dict\\n    rating: float\\n\\nEntity:\\n    Delivery\\n    id: integer\\n    order_id: integer\\n    driver_id: integer\\n    pickup_time: datetime\\n    delivery_time: datetime\\n    status: string\\n    route: list\\n\\nBehavior:\\n    BrowseMenu\\n    Input: restaurant_id integer, category string\\n    Output: items list\\n    Action: Return available menu items filtered by category\\n\\nBehavior:\\n    PlaceOrder\\n    Input: customer_id integer, restaurant_id integer, items list, delivery_address dict\\n    Output: order Order\\n    Action: Validate items, calculate total, and create order\\n\\nBehavior:\\n    DispatchDriver\\n    Input: order_id integer, restaurant_location dict\\n    Output: delivery Delivery\\n    Action: Find nearest available driver and assign delivery\\n\\nBehavior:\\n    UpdateDeliveryStatus\\n    Input: delivery_id integer, status string, location dict\\n    Output: updated_delivery Delivery\\n    Action: Update delivery progress and notify customer\\n\\nBehavior:\\n    CalculateDeliveryFee\\n    Input: distance float, order_total float, demand_level float\\n    Output: fee float\\n    Action: Compute delivery fee based on distance and demand\\n\\nCondition:\\n    When: order preparation exceeds estimated prep time by 10 minutes\\n    Then: Notify customer of delay and update delivery estimate\\n\\nCondition:\\n    When: no drivers available within delivery radius\\n    Then: Queue order and notify customer of extended wait time\\n\\nEvent:\\n    On: OrderPlaced\\n    Do: Send order to restaurant kitchen and start driver dispatch\\n\\nEvent:\\n    On: OrderReady\\n    Do: Notify assigned driver for pickup\\n\\nEvent:\\n    On: DeliveryCompleted\\n    Do: Process payment, request ratings, and update driver availability\\n\\nParallel:\\n    Orders\\n    Delivery\\n\\nOptimize:\\n    Driver matching speed\\n    Priority: Critical\\n\\nOptimize:\\n    Delivery route efficiency\\n    Priority: High\"}\n{\"id\": \"aicl_012\", \"domain\": \"cryptocurrency_exchange\", \"levels\": [1,2,3,4,5,6,7,8,9,10], \"description\": \"Build a cryptocurrency exchange with wallets, trading, order book, ledger, and compliance\", \"code\": \"# Cryptocurrency Exchange\\n# All 10 Levels: Digital asset trading platform\\n\\nGoal:\\nCreate a cryptocurrency exchange with wallet management, order book trading, real-time market data, transaction ledger, and regulatory compliance\\n\\nConstraint:\\nOrder matching must complete within 100 milliseconds\\n\\nConstraint:\\nMust comply with KYC and AML regulations\\n\\nConstraint:\\nAll transactions must be cryptographically verifiable\\n\\nRisk:\\nFlash crash or extreme market volatility\\n\\nRecovery:\\nActivate circuit breaker and halt trading for cooling period\\n\\nRisk:\\nWallet security breach\\n\\nRecovery:\\nFreeze affected wallets, initiate security audit, and notify authorities\\n\\nRisk:\\nDouble spend attack\\n\\nRecovery:\\nReject transaction, flag account, and increase confirmation requirements\\n\\nRisk:\\nExchange liquidity insufficient for large order\\n\\nRecovery:\\nSplit order across time and notify trader of execution risk\\n\\nLayer:\\n    Trading\\n    SubLayer:\\n        OrderBook\\n    SubLayer:\\n        Matching\\n\\nLayer:\\n    Wallets\\n    SubLayer:\\n        Balances\\n    SubLayer:\\n        Transactions\\n\\nLayer:\\n    Market\\n    SubLayer:\\n        Prices\\n    SubLayer:\\n        Analytics\\n\\nLayer:\\n    Compliance\\n    SubLayer:\\n        KYC\\n    SubLayer:\\n        AML\\n\\nValidation:\\nOrders are matched fairly by price and time priority\\n\\nValidation:\\nWallet balances are always consistent with transaction history\\n\\nValidation:\\nKYC verification is completed before trading is enabled\\n\\nValidation:\\nAll transactions are recorded in immutable ledger\\n\\nEntity:\\n    Wallet\\n    id: integer\\n    user_id: integer\\n    currency: string\\n    balance: float\\n    locked_balance: float\\n    address: string\\n\\nEntity:\\n    Order\\n    id: integer\\n    user_id: integer\\n    pair: string\\n    side: string\\n    type: string\\n    price: float\\n    amount: float\\n    filled: float\\n    status: string\\n    created_at: datetime\\n\\nEntity:\\n    Trade\\n    id: integer\\n    buy_order_id: integer\\n    sell_order_id: integer\\n    pair: string\\n    price: float\\n    amount: float\\n    executed_at: datetime\\n\\nEntity:\\n    Transaction\\n    id: integer\\n    wallet_id: integer\\n    type: string\\n    amount: float\\n    hash: string\\n    status: string\\n    created_at: datetime\\n\\nEntity:\\n    User\\n    id: integer\\n    email: string\\n    kyc_status: string\\n    trading_enabled: boolean\\n    risk_level: string\\n\\nBehavior:\\n    PlaceOrder\\n    Input: user_id integer, pair string, side string, type string, price float, amount float\\n    Output: order Order\\n    Action: Validate balance, create order, and submit to order book\\n\\nBehavior:\\n    MatchOrders\\n    Input: pair string, incoming_order Order\\n    Output: trades list\\n    Action: Match incoming order against book by price-time priority\\n\\nBehavior:\\n    ExecuteTrade\\n    Input: buy_order Order, sell_order Order, match_price float, match_amount float\\n    Output: trade Trade\\n    Action: Execute matched trade, update balances, and record transaction\\n\\nBehavior:\\n    DepositFunds\\n    Input: user_id integer, currency string, amount float, tx_hash string\\n    Output: transaction Transaction\\n    Action: Credit wallet after blockchain confirmation\\n\\nBehavior:\\n    WithdrawFunds\\n    Input: user_id integer, currency string, amount float, destination string\\n    Output: transaction Transaction\\n    Action: Debit wallet and submit withdrawal to blockchain\\n\\nBehavior:\\n    VerifyKYC\\n    Input: user_id integer, documents list\\n    Output: verification dict\\n    Action: Verify identity documents and enable trading if approved\\n\\nCondition:\\n    When: price moves more than 20 percent in 5 minutes\\n    Then: Activate circuit breaker and halt trading for 15 minutes\\n\\nCondition:\\n    When: user KYC is not verified and order amount exceeds daily limit\\n    Then: Block order and prompt user to complete KYC verification\\n\\nCondition:\\n    When: wallet balance is insufficient for order\\n    Then: Reject order and notify user of insufficient funds\\n\\nEvent:\\n    On: OrderPlaced\\n    Do: Add to order book and attempt matching\\n\\nEvent:\\n    On: TradeExecuted\\n    Do: Update both wallets, record in ledger, and publish market data\\n\\nEvent:\\n    On: DepositConfirmed\\n    Do: Credit wallet balance and notify user\\n\\nEvent:\\n    On: CircuitBreakerTriggered\\n    Do: Halt all trading and notify all users\\n\\nParallel:\\n    Trading\\n    Market\\n\\nOptimize:\\n    Order matching latency\\n    Priority: Critical\\n\\nOptimize:\\n    Market data refresh rate\\n    Priority: High\\n\\nLearn:\\n    fraud_detection\\n    Based: trading_patterns, withdrawal_frequency, ip_geolocation, device_fingerprint\\n    Adapt: increase verification requirements when fraud_probability exceeds 70 percent\\n\\nLearn:\\n    price_prediction\\n    Based: order_book_imbalance, volume_trends, market_sentiment, correlated_assets\\n    Adapt: adjust risk limits based on predicted volatility\\n\\nEncrypt:\\n    wallet private_keys\\n    user identity_documents\\n\\nProtect:\\n    Wallet balances from unauthorized modification\\n    Order book from manipulation\\n    Transaction history from tampering\\n\\nNative:\\n    blockchain_monitor\\n    Language: Python\\n    Code: \\\"import asyncio; from web3 import Web3; w3 = Web3(Web3.WebsocketProvider(WS_URL)); pending = w3.eth.filter('pending')\\\"\"}\n{\"id\": \"aicl_013\", \"domain\": \"weather_monitoring\", \"levels\": [1,2,3,4,5], \"description\": \"Build a weather monitoring system with stations, readings, alerts, and forecasts\", \"code\": \"# Weather Monitoring System\\n# Levels 1-5: Simple meteorological tracking\\n\\nGoal:\\nCreate a weather monitoring system with sensor stations, real-time readings, threshold alerts, and weather forecasts\\n\\nConstraint:\\nReadings must be collected every 5 minutes\\n\\nRisk:\\nSensor station goes offline\\n\\nRecovery:\\nUse last known reading and flag station for maintenance\\n\\nRisk:\\nExtreme weather alert missed\\n\\nRecovery:\\nTrigger redundant alert system and notify emergency services\\n\\nLayer:\\n    Stations\\n    SubLayer:\\n        Sensors\\n\\nLayer:\\n    Readings\\n    SubLayer:\\n        Collection\\n\\nLayer:\\n    Alerts\\n    SubLayer:\\n        Thresholds\\n\\nValidation:\\nReadings are collected at regular intervals\\n\\nValidation:\\nAlerts fire when thresholds are exceeded\\n\\nEntity:\\n    Station\\n    id: integer\\n    name: string\\n    location: string\\n    latitude: float\\n    longitude: float\\n    altitude: float\\n    is_active: boolean\\n\\nEntity:\\n    Reading\\n    id: integer\\n    station_id: integer\\n    temperature: float\\n    humidity: float\\n    pressure: float\\n    wind_speed: float\\n    wind_direction: string\\n    timestamp: datetime\\n\\nEntity:\\n    Alert\\n    id: integer\\n    station_id: integer\\n    type: string\\n    severity: string\\n    message: string\\n    is_active: boolean\\n    created_at: datetime\\n\\nBehavior:\\n    CollectReading\\n    Input: station_id integer, temperature float, humidity float, pressure float, wind_speed float\\n    Output: reading Reading\\n    Action: Store weather reading and check alert thresholds\\n\\nBehavior:\\n    CheckThresholds\\n    Input: reading Reading\\n    Output: alerts list\\n    Action: Compare reading values against configured thresholds\\n\\nBehavior:\\n    GenerateForecast\\n    Input: station_id integer, hours_ahead integer\\n    Output: forecast dict\\n    Action: Predict weather conditions based on recent trends\\n\\nBehavior:\\n    NotifyAlert\\n    Input: alert Alert\\n    Output: notification dict\\n    Action: Send alert to subscribers through configured channels\\n\\nCondition:\\n    When: temperature exceeds 40 degrees celsius\\n    Then: Issue heat wave warning and activate public safety protocol\\n\\nCondition:\\n    When: wind speed exceeds 90 km/h\\n    Then: Issue storm warning and recommend shelter\\n\\nEvent:\\n    On: ReadingCollected\\n    Do: Update dashboard and check alert thresholds\\n\\nEvent:\\n    On: AlertTriggered\\n    Do: Send notifications and log in alert history\"}\n{\"id\": \"aicl_014\", \"domain\": \"library_management\", \"levels\": [1,2,3,4,5,6], \"description\": \"Build a library management system with books, members, loans, reservations, and overdue tracking\", \"code\": \"# Library Management System\\n# Levels 1-6: Book lending with concurrency\\n\\nGoal:\\nCreate a library management system with book catalog, member registration, loan processing, reservation queue, overdue tracking, and fine calculation\\n\\nConstraint:\\nEach member may borrow up to 10 books simultaneously\\n\\nConstraint:\\nOverdue fines must be calculated daily\\n\\nRisk:\\nBook lost or damaged by member\\n\\nRecovery:\\nCharge replacement fee and update inventory\\n\\nRisk:\\nPopular book has long reservation queue\\n\\nRecovery:\\nReduce loan period for high-demand books and purchase additional copies\\n\\nRisk:\\nMember accumulates excessive fines\\n\\nRecovery:\\nSuspend borrowing privileges until fines are paid or arranged\\n\\nLayer:\\n    Catalog\\n    SubLayer:\\n        Books\\n    SubLayer:\\n        Search\\n\\nLayer:\\n    Members\\n    SubLayer:\\n        Registration\\n    SubLayer:\\n        Accounts\\n\\nLayer:\\n    Circulation\\n    SubLayer:\\n        Loans\\n    SubLayer:\\n        Returns\\n\\nLayer:\\n    Reservations\\n    SubLayer:\\n        Queue\\n    SubLayer:\\n        Notifications\\n\\nValidation:\\nBooks can be checked out and returned correctly\\n\\nValidation:\\nOverdue fines are calculated accurately\\n\\nValidation:\\nReservation queue operates fairly\\n\\nEntity:\\n    Book\\n    id: integer\\n    title: string\\n    author: string\\n    isbn: string\\n    category: string\\n    publication_year: integer\\n    status: string\\n    location: string\\n\\nEntity:\\n    Member\\n    id: integer\\n    name: string\\n    email: string\\n    phone: string\\n    membership_type: string\\n    books_borrowed: integer\\n    outstanding_fines: float\\n    status: string\\n\\nEntity:\\n    Loan\\n    id: integer\\n    book_id: integer\\n    member_id: integer\\n    checkout_date: datetime\\n    due_date: datetime\\n    return_date: datetime\\n    status: string\\n\\nEntity:\\n    Reservation\\n    id: integer\\n    book_id: integer\\n    member_id: integer\\n    position: integer\\n    created_at: datetime\\n    status: string\\n\\nEntity:\\n    Fine\\n    id: integer\\n    member_id: integer\\n    loan_id: integer\\n    amount: float\\n    days_overdue: integer\\n    status: string\\n\\nBehavior:\\n    CheckoutBook\\n    Input: book_id integer, member_id integer\\n    Output: loan Loan\\n    Action: Verify availability and member eligibility, create loan record\\n\\nBehavior:\\n    ReturnBook\\n    Input: loan_id integer\\n    Output: returned_loan Loan\\n    Action: Process return, calculate fines if overdue, check reservation queue\\n\\nBehavior:\\n    ReserveBook\\n    Input: book_id integer, member_id integer\\n    Output: reservation Reservation\\n    Action: Add member to reservation queue if book is unavailable\\n\\nBehavior:\\n    CalculateFine\\n    Input: loan_id integer\\n    Output: fine Fine\\n    Action: Compute daily overdue fine from due date to return date\\n\\nBehavior:\\n    SearchCatalog\\n    Input: query string, category string\\n    Output: books list\\n    Action: Search books by title, author, or ISBN with filters\\n\\nBehavior:\\n    RenewLoan\\n    Input: loan_id integer\\n    Output: renewed_loan Loan\\n    Action: Extend due date if no reservations exist for the book\\n\\nCondition:\\n    When: book is overdue by more than 30 days\\n    Then: Mark as lost and charge replacement fee to member\\n\\nCondition:\\n    When: member outstanding_fines exceed 25 dollars\\n    Then: Suspend borrowing privileges until payment is made\\n\\nCondition:\\n    When: reservation queue for a book exceeds 5 members\\n    Then: Reduce loan period to 7 days and order additional copy\\n\\nEvent:\\n    On: BookCheckedOut\\n    Do: Update book status, decrement member borrow count, set due date\\n\\nEvent:\\n    On: BookReturned\\n    Do: Update book status, increment member borrow count, notify next reservation\\n\\nEvent:\\n    On: ReservationAvailable\\n    Do: Notify member that reserved book is ready for pickup\\n\\nParallel:\\n    Catalog\\n    Circulation\"}\n{\"id\": \"aicl_015\", \"domain\": \"fitness_tracker\", \"levels\": [1,2,3,4,5,7,8], \"description\": \"Build a fitness tracker with workouts, goals, progress tracking, challenges, and adaptive coaching\", \"code\": \"# Fitness Tracker Application\\n# Levels 1-5, 7-8: Health and workout platform with ML coaching\\n\\nGoal:\\nCreate a fitness tracker with workout logging, goal setting, progress tracking, social challenges, and AI-powered adaptive coaching\\n\\nConstraint:\\nWorkout data must sync across devices within 5 seconds\\n\\nConstraint:\\nHeart rate alerts must trigger within 3 seconds\\n\\nRisk:\\nUser overexertion detected via heart rate\\n\\nRecovery:\\nAlert user to stop exercise and display recovery breathing guide\\n\\nRisk:\\nGoal progress stagnation for over 2 weeks\\n\\nRecovery:\\nSuggest program variation and reduce intensity targets\\n\\nRisk:\\nSync conflict between devices\\n\\nRecovery:\\nMerge data with server-side conflict resolution preferring most recent\\n\\nLayer:\\n    Workouts\\n    SubLayer:\\n        Logging\\n    SubLayer:\\n        Analysis\\n\\nLayer:\\n    Goals\\n    SubLayer:\\n        Setting\\n    SubLayer:\\n        Progress\\n\\nLayer:\\n    Social\\n    SubLayer:\\n        Challenges\\n    SubLayer:\\n        Leaderboards\\n\\nValidation:\\nWorkouts are logged accurately with duration and metrics\\n\\nValidation:\\nGoal progress is calculated correctly\\n\\nValidation:\\nChallenges track participant progress fairly\\n\\nEntity:\\n    User\\n    id: integer\\n    name: string\\n    age: integer\\n    weight: float\\n    height: float\\n    fitness_level: string\\n    resting_heart_rate: integer\\n\\nEntity:\\n    Workout\\n    id: integer\\n    user_id: integer\\n    type: string\\n    duration: integer\\n    calories: integer\\n    heart_rate_avg: integer\\n    heart_rate_max: integer\\n    distance: float\\n    notes: string\\n    completed_at: datetime\\n\\nEntity:\\n    Goal\\n    id: integer\\n    user_id: integer\\n    metric: string\\n    target_value: float\\n    current_value: float\\n    deadline: datetime\\n    status: string\\n\\nEntity:\\n    Challenge\\n    id: integer\\n    name: string\\n    metric: string\\n    target_value: float\\n    start_date: datetime\\n    end_date: datetime\\n    participants: list\\n\\nEntity:\\n    Achievement\\n    id: integer\\n    user_id: integer\\n    type: string\\n    name: string\\n    earned_at: datetime\\n\\nBehavior:\\n    LogWorkout\\n    Input: user_id integer, type string, duration integer, metrics dict\\n    Output: workout Workout\\n    Action: Record workout data and update goal progress\\n\\nBehavior:\\n    SetGoal\\n    Input: user_id integer, metric string, target float, deadline datetime\\n    Output: goal Goal\\n    Action: Create goal with deadline and initial progress\\n\\nBehavior:\\n    UpdateProgress\\n    Input: goal_id integer, new_value float\\n    Output: updated_goal Goal\\n    Action: Update goal progress and check if achieved\\n\\nBehavior:\\n    JoinChallenge\\n    Input: user_id integer, challenge_id integer\\n    Output: participation dict\\n    Action: Register user for challenge and initialize progress\\n\\nBehavior:\\n    GenerateCoachingPlan\\n    Input: user_id integer, goals list, fitness_level string\\n    Output: plan dict\\n    Action: Create personalized workout plan based on goals and history\\n\\nBehavior:\\n    CalculateCalories\\n    Input: user User, workout_type string, duration integer, intensity string\\n    Output: calories integer\\n    Action: Estimate calorie burn using user metrics and activity data\\n\\nCondition:\\n    When: heart rate exceeds 90 percent of age-predicted maximum\\n    Then: Alert user to reduce intensity and display recovery zone\\n\\nCondition:\\n    When: goal deadline is within 7 days and progress below 70 percent\\n    Then: Suggest revised target or intensified workout schedule\\n\\nCondition:\\n    When: workout streak reaches 30 consecutive days\\n    Then: Award achievement badge and share milestone\\n\\nEvent:\\n    On: WorkoutCompleted\\n    Do: Update goals, check achievements, and refresh coaching plan\\n\\nEvent:\\n    On: GoalAchieved\\n    Do: Award achievement, notify followers, and suggest next goal\\n\\nEvent:\\n    On: ChallengeCompleted\\n    Do: Rank participants and distribute rewards\\n\\nOptimize:\\n    Data sync speed\\n    Priority: Critical\\n\\nOptimize:\\n    Coaching plan relevance\\n    Priority: High\\n\\nLearn:\\n    performance_prediction\\n    Based: workout_history, recovery_time, sleep_data, nutrition_log\\n    Adapt: adjust workout intensity and recovery periods based on predicted readiness\\n\\nLearn:\\n    plate_detection\\n    Based: progress_trend, workout_variety, heart_rate_response, subjective_fatigue\\n    Adapt: recommend program deload or variation when plateau probability exceeds 60 percent\"}\n{\"id\": \"aicl_016\", \"domain\": \"real_estate\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a real estate platform with property listings, agents, viewings, offers, and mortgage calculation\", \"code\": \"# Real Estate Platform\\n# Levels 1-7: Property marketplace with scheduling\\n\\nGoal:\\nCreate a real estate platform with property listings, agent management, viewing scheduling, offer management, and mortgage calculation\\n\\nConstraint:\\nProperty search must return results within 1 second\\n\\nConstraint:\\nViewing confirmations must be sent within 30 seconds\\n\\nRisk:\\nProperty listing contains inaccurate information\\n\\nRecovery:\\nFlag listing for review and notify agent to verify details\\n\\nRisk:\\nViewing schedule conflict\\n\\nRecovery:\\nDetect overlap and propose alternative time slots\\n\\nRisk:\\nOffer below minimum acceptable price\\n\\nRecovery:\\nAuto-reject and inform buyer of minimum threshold\\n\\nLayer:\\n    Properties\\n    SubLayer:\\n        Listings\\n    SubLayer:\\n        Search\\n\\nLayer:\\n    Agents\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Schedule\\n\\nLayer:\\n    Transactions\\n    SubLayer:\\n        Viewings\\n    SubLayer:\\n        Offers\\n\\nValidation:\\nProperties can be listed with accurate details and photos\\n\\nValidation:\\nViewings can be scheduled without conflicts\\n\\nValidation:\\nOffers are tracked and negotiated correctly\\n\\nEntity:\\n    Property\\n    id: integer\\n    address: string\\n    price: float\\n    bedrooms: integer\\n    bathrooms: integer\\n    square_feet: integer\\n    property_type: string\\n    status: string\\n    agent_id: integer\\n    features: list\\n    photos: list\\n\\nEntity:\\n    Agent\\n    id: integer\\n    name: string\\n    email: string\\n    phone: string\\n    license_number: string\\n    specialization: string\\n    rating: float\\n    listings_count: integer\\n\\nEntity:\\n    Viewing\\n    id: integer\\n    property_id: integer\\n    buyer_id: integer\\n    agent_id: integer\\n    scheduled_at: datetime\\n    duration: integer\\n    status: string\\n    notes: string\\n\\nEntity:\\n    Offer\\n    id: integer\\n    property_id: integer\\n    buyer_id: integer\\n    amount: float\\n    conditions: list\\n    status: string\\n    submitted_at: datetime\\n    response_deadline: datetime\\n\\nEntity:\\n    Buyer\\n    id: integer\\n    name: string\\n    email: string\\n    phone: string\\n    budget_max: float\\n    preferred_areas: list\\n    pre_approved: boolean\\n\\nBehavior:\\n    ListProperty\\n    Input: address string, price float, details dict, agent_id integer\\n    Output: property Property\\n    Action: Create property listing with details and assign to agent\\n\\nBehavior:\\n    ScheduleViewing\\n    Input: property_id integer, buyer_id integer, agent_id integer, preferred_time datetime\\n    Output: viewing Viewing\\n    Action: Check availability and schedule property viewing\\n\\nBehavior:\\n    SubmitOffer\\n    Input: property_id integer, buyer_id integer, amount float, conditions list\\n    Output: offer Offer\\n    Action: Submit purchase offer and notify listing agent\\n\\nBehavior:\\n    CalculateMortgage\\n    Input: price float, down_payment float, rate float, term_years integer\\n    Output: mortgage dict\\n    Action: Calculate monthly payment, total interest, and amortization schedule\\n\\nBehavior:\\n    SearchProperties\\n    Input: criteria dict, page integer\\n    Output: properties list\\n    Action: Filter properties matching search criteria\\n\\nCondition:\\n    When: offer amount is below 90 percent of listing price\\n    Then: Flag as lowball offer and advise agent on counter-offer strategy\\n\\nCondition:\\n    When: property has been listed for over 90 days\\n    Then: Suggest price reduction to agent and seller\\n\\nEvent:\\n    On: PropertyListed\\n    Do: Index for search, notify matching buyers, and add to agent portfolio\\n\\nEvent:\\n    On: OfferSubmitted\\n    Do: Notify seller agent and start response timer\\n\\nEvent:\\n    On: ViewingCompleted\\n    Do: Collect feedback from buyer and update agent notes\\n\\nParallel:\\n    Properties\\n    Transactions\\n\\nOptimize:\\n    Search performance\\n    Priority: Critical\\n\\nOptimize:\\n    Mortgage calculation accuracy\\n    Priority: High\"}\n{\"id\": \"aicl_017\", \"domain\": \"supply_chain\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a supply chain logistics system with shipments, routes, warehouses, customs, and predictive analytics\", \"code\": \"# Supply Chain Logistics\\n# Levels 1-8: Global shipping with predictive analytics\\n\\nGoal:\\nCreate a supply chain logistics system with shipment tracking, route optimization, warehouse management, customs processing, and predictive delay analytics\\n\\nConstraint:\\nShipment tracking must update every 15 minutes\\n\\nConstraint:\\nCustoms documentation must be generated automatically\\n\\nRisk:\\nShipment delayed at customs\\n\\nRecovery:\\nEngage customs broker and notify consignee of revised ETA\\n\\nRisk:\\nWarehouse capacity exceeded during peak season\\n\\nRecovery:\\nActivate overflow warehouse and reroute incoming shipments\\n\\nRisk:\\nRoute disruption due to weather or port closure\\n\\nRecovery:\\nRecalculate route and reroute through alternative port\\n\\nRisk:\\nSupplier fails to deliver on schedule\\n\\nRecovery:\\nActivate backup supplier and adjust production timeline\\n\\nLayer:\\n    Shipments\\n    SubLayer:\\n        Tracking\\n    SubLayer:\\n        Documentation\\n\\nLayer:\\n    Routes\\n    SubLayer:\\n        Planning\\n    SubLayer:\\n        Optimization\\n\\nLayer:\\n    Warehouses\\n    SubLayer:\\n        Capacity\\n    SubLayer:\\n        Operations\\n\\nLayer:\\n    Compliance\\n    SubLayer:\\n        Customs\\n    SubLayer:\\n        Regulations\\n\\nValidation:\\nShipments are tracked from origin to destination\\n\\nValidation:\\nRoutes are optimized for cost and time\\n\\nValidation:\\nCustoms documentation is complete and accurate\\n\\nEntity:\\n    Shipment\\n    id: integer\\n    origin: string\\n    destination: string\\n    status: string\\n    carrier: string\\n    weight: float\\n    value: float\\n    estimated_arrival: datetime\\n    actual_arrival: datetime\\n\\nEntity:\\n    Route\\n    id: integer\\n    origin: string\\n    destination: string\\n    waypoints: list\\n    distance: float\\n    estimated_duration: integer\\n    mode: string\\n\\nEntity:\\n    Warehouse\\n    id: integer\\n    name: string\\n    location: string\\n    capacity: integer\\n    current_usage: integer\\n    temperature_controlled: boolean\\n\\nEntity:\\n    CustomsEntry\\n    id: integer\\n    shipment_id: integer\\n    country: string\\n    status: string\\n    documents: list\\n    duty_amount: float\\n    cleared_at: datetime\\n\\nEntity:\\n    Supplier\\n    id: integer\\n    name: string\\n    country: string\\n    lead_time_days: integer\\n    reliability_score: float\\n    products: list\\n\\nBehavior:\\n    CreateShipment\\n    Input: origin string, destination string, items list, carrier string\\n    Output: shipment Shipment\\n    Action: Create shipment record with tracking number and documentation\\n\\nBehavior:\\n    OptimizeRoute\\n    Input: origin string, destination string, constraints dict\\n    Output: route Route\\n    Action: Calculate optimal route considering cost, time, and risk factors\\n\\nBehavior:\\n    TrackShipment\\n    Input: shipment_id integer\\n    Output: status dict\\n    Action: Retrieve current location and status of shipment\\n\\nBehavior:\\n    ProcessCustoms\\n    Input: shipment_id integer, country string, documents list\\n    Output: entry CustomsEntry\\n    Action: Submit customs documentation and track clearance status\\n\\nBehavior:\\n    ManageCapacity\\n    Input: warehouse_id integer, incoming integer, outgoing integer\\n    Output: capacity dict\\n    Action: Update warehouse capacity and trigger overflow if needed\\n\\nCondition:\\n    When: shipment is delayed by more than 48 hours\\n    Then: Notify consignee, activate backup route, and adjust downstream schedule\\n\\nCondition:\\n    When: warehouse usage exceeds 90 percent capacity\\n    Then: Activate overflow facility and reroute incoming shipments\\n\\nCondition:\\n    When: customs clearance exceeds 5 business days\\n    Then: Engage customs broker and escalate to trade compliance team\\n\\nEvent:\\n    On: ShipmentDeparted\\n    Do: Start tracking updates and notify consignee of departure\\n\\nEvent:\\n    On: CustomsCleared\\n    Do: Update shipment status and schedule final delivery\\n\\nEvent:\\n    On: DeliveryCompleted\\n    Do: Close shipment record and update supplier reliability score\\n\\nParallel:\\n    Shipments\\n    Routes\\n\\nOptimize:\\n    Route calculation speed\\n    Priority: High\\n\\nOptimize:\\n    Warehouse utilization\\n    Priority: High\\n\\nLearn:\\n    delay_prediction\\n    Based: route_history, carrier_performance, weather_data, port_congestion, season\\n    Adapt: add buffer time to routes with high delay probability and pre-clear customs\"}\n{\"id\": \"aicl_018\", \"domain\": \"event_ticketing\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build an event ticketing system with events, venues, tickets, seating, and pricing\", \"code\": \"# Event Ticketing System\\n# Levels 1-7: Ticket sales with dynamic pricing\\n\\nGoal:\\nCreate an event ticketing system with event management, venue seating, ticket sales, dynamic pricing, and entry management\\n\\nConstraint:\\nTicket purchase must complete within 10 seconds\\n\\nConstraint:\\nSeating availability must be real-time accurate\\n\\nRisk:\\nTicket scalping or bot purchases\\n\\nRecovery:\\nImplement purchase limits, CAPTCHA, and queue system for high-demand events\\n\\nRisk:\\nEvent cancellation\\n\\nRecovery:\\nProcess full refunds automatically and notify all ticket holders\\n\\nRisk:\\nOverselling seats due to concurrent purchases\\n\\nRecovery:\\nImplement row-level locking and hold seats during checkout\\n\\nLayer:\\n    Events\\n    SubLayer:\\n        Management\\n    SubLayer:\\n        Schedule\\n\\nLayer:\\n    Venues\\n    SubLayer:\\n        Seating\\n    SubLayer:\\n        Layout\\n\\nLayer:\\n    Tickets\\n    SubLayer:\\n        Sales\\n    SubLayer:\\n        Pricing\\n\\nLayer:\\n    Entry\\n    SubLayer:\\n        Validation\\n    SubLayer:\\n        Access\\n\\nValidation:\\nTickets can be purchased and transferred correctly\\n\\nValidation:\\nSeating availability is accurate in real-time\\n\\nValidation:\\nDynamic pricing reflects demand correctly\\n\\nEntity:\\n    Event\\n    id: integer\\n    name: string\\n    venue_id: integer\\n    date: datetime\\n    category: string\\n    status: string\\n    base_price: float\\n    total_capacity: integer\\n    tickets_sold: integer\\n\\nEntity:\\n    Venue\\n    id: integer\\n    name: string\\n    address: string\\n    capacity: integer\\n    sections: list\\n    layout: dict\\n\\nEntity:\\n    Ticket\\n    id: integer\\n    event_id: integer\\n    section: string\\n    row: string\\n    seat: string\\n    price: float\\n    status: string\\n    holder_name: string\\n    purchased_at: datetime\\n\\nEntity:\\n    PricingTier\\n    id: integer\\n    event_id: integer\\n    section: string\\n    base_price: float\\n    current_price: float\\n    demand_level: string\\n\\nEntity:\\n    Attendee\\n    id: integer\\n    name: string\\n    email: string\\n    tickets: list\\n\\nBehavior:\\n    CreateEvent\\n    Input: name string, venue_id integer, date datetime, base_price float\\n    Output: event Event\\n    Action: Create event with venue assignment and generate seating inventory\\n\\nBehavior:\\n    PurchaseTicket\\n    Input: event_id integer, section string, row string, seat string, buyer_name string\\n    Output: ticket Ticket\\n    Action: Reserve seat, process payment, and issue ticket\\n\\nBehavior:\\n    UpdatePricing\\n    Input: event_id integer, demand_data dict\\n    Output: updated_tiers list\\n    Action: Adjust prices based on demand level and time to event\\n\\nBehavior:\\n    ValidateTicket\\n    Input: ticket_id integer, event_id integer\\n    Output: validation dict\\n    Action: Check ticket validity and mark as used\\n\\nBehavior:\\n    TransferTicket\\n    Input: ticket_id integer, new_holder_name string\\n    Output: updated_ticket Ticket\\n    Action: Transfer ticket to new holder with verification\\n\\nCondition:\\n    When: tickets sold exceeds 80 percent of capacity\\n    Then: Increase remaining ticket prices by surge factor\\n\\nCondition:\\n    When: single user attempts to purchase more than 4 tickets\\n    Then: Flag for bot detection and require additional verification\\n\\nCondition:\\n    When: event is within 24 hours and tickets remain unsold\\n    Then: Reduce prices to fill remaining capacity\\n\\nEvent:\\n    On: TicketPurchased\\n    Do: Update seating availability, adjust pricing, and send confirmation\\n\\nEvent:\\n    On: EventCancelled\\n    Do: Process refunds for all ticket holders and send cancellation notice\\n\\nEvent:\\n    On: TicketValidated\\n    Do: Mark ticket as used and update entry count\\n\\nParallel:\\n    Events\\n    Tickets\\n\\nOptimize:\\n    Ticket purchase speed\\n    Priority: Critical\\n\\nOptimize:\\n    Pricing algorithm accuracy\\n    Priority: High\"}\n{\"id\": \"aicl_019\", \"domain\": \"veterinary_clinic\", \"levels\": [1,2,3,4,5], \"description\": \"Build a veterinary clinic system with pets, owners, appointments, and treatments\", \"code\": \"# Veterinary Clinic System\\n# Levels 1-5: Pet healthcare management\\n\\nGoal:\\nCreate a veterinary clinic system with pet records, owner management, appointment scheduling, treatment tracking, and vaccination reminders\\n\\nConstraint:\\nVaccination reminders must be sent 2 weeks before due date\\n\\nRisk:\\nPet allergic reaction to medication\\n\\nRecovery:\\nAdminister emergency treatment and document in pet record\\n\\nRisk:\\nAppointment no-show\\n\\nRecovery:\\nContact owner and offer rescheduling within 24 hours\\n\\nLayer:\\n    Patients\\n    SubLayer:\\n        Pets\\n    SubLayer:\\n        Owners\\n\\nLayer:\\n    Schedule\\n    SubLayer:\\n        Appointments\\n\\nLayer:\\n    Medical\\n    SubLayer:\\n        Treatments\\n    SubLayer:\\n        Vaccinations\\n\\nValidation:\\nPets are registered with complete medical history\\n\\nValidation:\\nAppointments are scheduled without conflicts\\n\\nValidation:\\nVaccination reminders are sent on time\\n\\nEntity:\\n    Pet\\n    id: integer\\n    name: string\\n    species: string\\n    breed: string\\n    date_of_birth: datetime\\n    weight: float\\n    owner_id: integer\\n    allergies: list\\n\\nEntity:\\n    Owner\\n    id: integer\\n    name: string\\n    email: string\\n    phone: string\\n    address: string\\n    pets: list\\n\\nEntity:\\n    Appointment\\n    id: integer\\n    pet_id: integer\\n    vet_id: integer\\n    datetime: datetime\\n    reason: string\\n    status: string\\n    notes: string\\n\\nEntity:\\n    Treatment\\n    id: integer\\n    pet_id: integer\\n    date: datetime\\n    diagnosis: string\\n    procedure: string\\n    medications: list\\n    cost: float\\n\\nEntity:\\n    Vaccination\\n    id: integer\\n    pet_id: integer\\n    vaccine: string\\n    date_administered: datetime\\n    next_due: datetime\\n\\nBehavior:\\n    RegisterPet\\n    Input: name string, species string, breed string, owner_id integer\\n    Output: pet Pet\\n    Action: Create pet record with owner association\\n\\nBehavior:\\n    ScheduleAppointment\\n    Input: pet_id integer, vet_id integer, datetime datetime, reason string\\n    Output: appointment Appointment\\n    Action: Find available slot and book appointment\\n\\nBehavior:\\n    RecordTreatment\\n    Input: pet_id integer, diagnosis string, procedure string, medications list\\n    Output: treatment Treatment\\n    Action: Document treatment and update pet medical history\\n\\nBehavior:\\n    AdministerVaccination\\n    Input: pet_id integer, vaccine string\\n    Output: vaccination Vaccination\\n    Action: Record vaccination and schedule next due date\\n\\nBehavior:\\n    SendReminder\\n    Input: owner_id integer, message string, type string\\n    Output: notification dict\\n    Action: Send appointment or vaccination reminder to owner\\n\\nCondition:\\n    When: vaccination is due within 14 days\\n    Then: Send reminder to owner and flag in pet record\\n\\nCondition:\\n    When: pet weight changes by more than 15 percent since last visit\\n    Then: Flag for dietary consultation at next appointment\\n\\nEvent:\\n    On: AppointmentBooked\\n    Do: Send confirmation to owner and add to vet schedule\\n\\nEvent:\\n    On: VaccinationDue\\n    Do: Send reminder and offer scheduling options\"}\n{\"id\": \"aicl_020\", \"domain\": \"game_server\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a multiplayer game server with players, matches, scores, leaderboards, and anti-cheat\", \"code\": \"# Multiplayer Game Server\\n# Levels 1-8: Game backend with ML anti-cheat\\n\\nGoal:\\nCreate a multiplayer game server with player management, matchmaking, match execution, scoring, leaderboards, and AI-powered anti-cheat detection\\n\\nConstraint:\\nMatchmaking must find opponents within 30 seconds\\n\\nConstraint:\\nGame state must sync at 60 ticks per second\\n\\nRisk:\\nPlayer using cheats or exploits\\n\\nRecovery:\\nFlag for review, shadow-ban suspicious player, and replay analysis\\n\\nRisk:\\nServer overload during peak hours\\n\\nRecovery:\\nSpin up additional server instances and queue incoming players\\n\\nRisk:\\nMatch desync between players\\n\\nRecovery:\\nRollback to last synchronized state and resimulate\\n\\nLayer:\\n    Players\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Inventory\\n\\nLayer:\\n    Matches\\n    SubLayer:\\n        Matchmaking\\n    SubLayer:\\n        Execution\\n\\nLayer:\\n    Competition\\n    SubLayer:\\n        Leaderboards\\n    SubLayer:\\n        Seasons\\n\\nLayer:\\n    Security\\n    SubLayer:\\n        AntiCheat\\n    SubLayer:\\n        Monitoring\\n\\nValidation:\\nPlayers can find and join matches quickly\\n\\nValidation:\\nScores and rankings are accurate and tamper-proof\\n\\nValidation:\\nAnti-cheat system detects violations\\n\\nEntity:\\n    Player\\n    id: integer\\n    username: string\\n    rank: string\\n    rating: integer\\n    wins: integer\\n    losses: integer\\n    created_at: datetime\\n    is_banned: boolean\\n\\nEntity:\\n    Match\\n    id: integer\\n    players: list\\n    status: string\\n    map: string\\n    mode: string\\n    started_at: datetime\\n    duration: integer\\n    winner_id: integer\\n\\nEntity:\\n    Score\\n    id: integer\\n    match_id: integer\\n    player_id: integer\\n    points: integer\\n    kills: integer\\n    deaths: integer\\n    assists: integer\\n\\nEntity:\\n    LeaderboardEntry\\n    player_id: integer\\n    season_id: integer\\n    rating: integer\\n    rank: integer\\n    wins: integer\\n    losses: integer\\n\\nEntity:\\n    CheatReport\\n    id: integer\\n    player_id: integer\\n    reporter_id: integer\\n    match_id: integer\\n    type: string\\n    evidence: dict\\n    status: string\\n\\nBehavior:\\n    RegisterPlayer\\n    Input: username string, email string\\n    Output: player Player\\n    Action: Create player profile with default rank and rating\\n\\nBehavior:\\n    FindMatch\\n    Input: player_id integer, mode string, region string\\n    Output: match Match\\n    Action: Match player with similar-skilled opponents in region\\n\\nBehavior:\\n    StartMatch\\n    Input: players list, map string, mode string\\n    Output: match Match\\n    Action: Initialize game state and begin match simulation\\n\\nBehavior:\\n    RecordScore\\n    Input: match_id integer, player_id integer, stats dict\\n    Output: score Score\\n    Action: Record match performance and update player rating\\n\\nBehavior:\\n    UpdateLeaderboard\\n    Input: season_id integer, player_id integer, result string\\n    Output: entry LeaderboardEntry\\n    Action: Update seasonal leaderboard with match result\\n\\nBehavior:\\n    DetectCheat\\n    Input: player_id integer, match_data dict\\n    Output: report CheatReport\\n    Action: Analyze match data for anomalies and flag suspicious behavior\\n\\nCondition:\\n    When: player win rate exceeds 85 percent over 50 matches\\n    Then: Flag for anti-cheat review and increase monitoring level\\n\\nCondition:\\n    When: matchmaking queue exceeds 60 seconds\\n    Then: Expand skill range and region to find opponents\\n\\nCondition:\\n    When: player reports exceed 5 in 24 hours\\n    Then: Auto-suspend pending investigation\\n\\nEvent:\\n    On: MatchCompleted\\n    Do: Calculate ratings, update leaderboard, and distribute rewards\\n\\nEvent:\\n    On: CheatDetected\\n    Do: Log evidence, notify player, and apply sanctions\\n\\nEvent:\\n    On: SeasonEnded\\n    Do: Finalize rankings, distribute rewards, and reset for new season\\n\\nParallel:\\n    Matches\\n    Security\\n\\nOptimize:\\n    Matchmaking speed\\n    Priority: Critical\\n\\nOptimize:\\n    Game state sync latency\\n    Priority: Critical\\n\\nLearn:\\n    cheat_detection\\n    Based: performance_metrics, input_patterns, timing_analysis, hardware_fingerprint\\n    Adapt: adjust detection thresholds based on false positive feedback\"}\n{\"id\": \"aicl_021\", \"domain\": \"chess_game\", \"levels\": [1,2,3,4,5,6], \"description\": \"Build a chess game with board state, moves, validation, and game flow\", \"code\": \"# Chess Game\\n# Levels 1-6: Complete chess with concurrency\\n\\nGoal:\\nCreate a chess game with board state management, move validation, check and checkmate detection, game history, and player timer management\\n\\nConstraint:\\nMove validation must complete within 10 milliseconds\\n\\nConstraint:\\nTimer accuracy must be within 100 milliseconds\\n\\nRisk:\\nPlayer disconnects during game\\n\\nRecovery:\\nPause game and allow 5-minute reconnection window\\n\\nRisk:\\nInvalid move submitted\\n\\nRecovery:\\nReject move and notify player of legal alternatives\\n\\nLayer:\\n    Game\\n    SubLayer:\\n        Board\\n    SubLayer:\\n        Rules\\n\\nLayer:\\n    Players\\n    SubLayer:\\n        White\\n    SubLayer:\\n        Black\\n\\nLayer:\\n    History\\n    SubLayer:\\n        Moves\\n    SubLayer:\\n        Positions\\n\\nValidation:\\nAll moves are validated against chess rules\\n\\nValidation:\\nCheck and checkmate are detected correctly\\n\\nValidation:\\nTimer counts down accurately\\n\\nEntity:\\n    GameState\\n    id: integer\\n    board: dict\\n    current_turn: string\\n    move_count: integer\\n    status: string\\n    en_passant_target: string\\n    castling_rights: dict\\n\\nEntity:\\n    Player\\n    id: integer\\n    name: string\\n    color: string\\n    rating: integer\\n    remaining_time: integer\\n\\nEntity:\\n    Move\\n    id: integer\\n    game_id: integer\\n    from_square: string\\n    to_square: string\\n    piece: string\\n    captured: string\\n    is_check: boolean\\n    is_checkmate: boolean\\n    notation: string\\n    timestamp: datetime\\n\\nEntity:\\n    ChessPiece\\n    position: string\\n    type: string\\n    color: string\\n    has_moved: boolean\\n\\nBehavior:\\n    InitializeBoard\\n    Input: variant string\\n    Output: state GameState\\n    Action: Set up standard chess starting position\\n\\nBehavior:\\n    ValidateMove\\n    Input: game_id integer, from_square string, to_square string\\n    Output: valid boolean\\n    Action: Check move against chess rules including check constraints\\n\\nBehavior:\\n    MakeMove\\n    Input: game_id integer, from_square string, to_square string, promotion string\\n    Output: move Move\\n    Action: Execute move, update board, and check game state\\n\\nBehavior:\\n    DetectCheck\\n    Input: game_id integer, color string\\n    Output: in_check boolean\\n    Action: Determine if the specified color king is in check\\n\\nBehavior:\\n    DetectCheckmate\\n    Input: game_id integer, color string\\n    Output: is_checkmate boolean\\n    Action: Check if color has no legal moves while in check\\n\\nBehavior:\\n    UpdateTimer\\n    Input: game_id integer, color string, elapsed integer\\n    Output: remaining integer\\n    Action: Decrement timer and flag if time expires\\n\\nCondition:\\n    When: player timer reaches zero\\n    Then: Player loses on time and game ends\\n\\nCondition:\\n    When: position repeats 3 times\\n    Then: Declare draw by threefold repetition\\n\\nCondition:\\n    When: 50 moves occur without pawn move or capture\\n    Then: Declare draw by fifty-move rule\\n\\nEvent:\\n    On: MoveMade\\n    Do: Update board display, switch turn, and check for check or checkmate\\n\\nEvent:\\n    On: CheckmateDetected\\n    Do: End game, update ratings, and record result\\n\\nEvent:\\n    On: DrawOffered\\n    Do: Present offer to opponent and handle response\\n\\nParallel:\\n    Game\\n    Players\"}\n{\"id\": \"aicl_022\", \"domain\": \"parking_system\", \"levels\": [1,2,3,4,5], \"description\": \"Build a smart parking system with spots, reservations, payments, and occupancy tracking\", \"code\": \"# Smart Parking System\\n# Levels 1-5: Urban parking management\\n\\nGoal:\\nCreate a smart parking system with spot management, reservation booking, payment processing, occupancy tracking, and dynamic pricing\\n\\nConstraint:\\nSpot availability must update within 5 seconds of change\\n\\nRisk:\\nVehicle overstays reservation\\n\\nRecovery:\\nCharge overtime rate and notify driver via app\\n\\nRisk:\\nSensor malfunction gives false availability\\n\\nRecovery:\\nFlag spot for manual verification and mark as unavailable\\n\\nLayer:\\n    Parking\\n    SubLayer:\\n        Spots\\n    SubLayer:\\n        Sensors\\n\\nLayer:\\n    Reservations\\n    SubLayer:\\n        Booking\\n    SubLayer:\\n        Payment\\n\\nValidation:\\nSpot availability is accurate and up-to-date\\n\\nValidation:\\nReservations are processed correctly\\n\\nEntity:\\n    ParkingSpot\\n    id: integer\\n    zone: string\\n    level: integer\\n    number: string\\n    type: string\\n    is_occupied: boolean\\n    hourly_rate: float\\n    last_updated: datetime\\n\\nEntity:\\n    Reservation\\n    id: integer\\n    spot_id: integer\\n    vehicle_plate: string\\n    start_time: datetime\\n    end_time: datetime\\n    status: string\\n    total_cost: float\\n\\nEntity:\\n    Vehicle\\n    plate: string\\n    make: string\\n    color: string\\n    owner_id: integer\\n\\nEntity:\\n    Payment\\n    id: integer\\n    reservation_id: integer\\n    amount: float\\n    method: string\\n    status: string\\n    processed_at: datetime\\n\\nBehavior:\\n    FindSpot\\n    Input: zone string, type string, start_time datetime, duration integer\\n    Output: spot ParkingSpot\\n    Action: Search available spots matching criteria\\n\\nBehavior:\\n    ReserveSpot\\n    Input: spot_id integer, vehicle_plate string, start_time datetime, end_time datetime\\n    Output: reservation Reservation\\n    Action: Book spot and process prepayment\\n\\nBehavior:\\n    ProcessPayment\\n    Input: reservation_id integer, method string\\n    Output: payment Payment\\n    Action: Calculate and charge parking fee\\n\\nBehavior:\\n    UpdateOccupancy\\n    Input: spot_id integer, is_occupied boolean\\n    Output: updated_spot ParkingSpot\\n    Action: Update spot occupancy from sensor data\\n\\nCondition:\\n    When: parking zone occupancy exceeds 90 percent\\n    Then: Increase hourly rate by surge factor\\n\\nCondition:\\n    When: vehicle overstays reservation by more than 15 minutes\\n    Then: Charge overtime rate at 150 percent of standard rate\\n\\nEvent:\\n    On: SpotOccupied\\n    Do: Update availability display and confirm reservation\\n\\nEvent:\\n    On: ReservationExpiring\\n    Do: Send notification to driver 15 minutes before end time\"}\n{\"id\": \"aicl_023\", \"domain\": \"crop_farming\", \"levels\": [1,2,3,4,5,7,8], \"description\": \"Build a smart farming system with crops, fields, irrigation, and yield prediction\", \"code\": \"# Smart Crop Farming System\\n# Levels 1-5, 7-8: Agricultural management with ML\\n\\nGoal:\\nCreate a smart farming system with crop planning, field management, irrigation control, harvest tracking, and yield prediction\\n\\nConstraint:\\nIrrigation must respond to soil moisture within 30 minutes\\n\\nConstraint:\\nWeather alerts must be processed within 5 minutes\\n\\nRisk:\\nFrost damage to crops\\n\\nRecovery:\\nActivate frost protection irrigation and deploy covers\\n\\nRisk:\\nPest or disease outbreak\\n\\nRecovery:\\nIsolate affected area and apply targeted treatment\\n\\nRisk:\\nIrrigation system failure\\n\\nRecovery:\\nSwitch to backup water source and alert maintenance\\n\\nLayer:\\n    Fields\\n    SubLayer:\\n        Zones\\n    SubLayer:\\n        Sensors\\n\\nLayer:\\n    Crops\\n    SubLayer:\\n        Planting\\n    SubLayer:\\n        Growth\\n\\nLayer:\\n    Irrigation\\n    SubLayer:\\n        Control\\n    SubLayer:\\n        Scheduling\\n\\nValidation:\\nField conditions are monitored continuously\\n\\nValidation:\\nIrrigation activates based on soil moisture thresholds\\n\\nValidation:\\nYield predictions improve over time\\n\\nEntity:\\n    Field\\n    id: integer\\n    name: string\\n    area_hectares: float\\n    soil_type: string\\n    current_crop: string\\n    zone_count: integer\\n\\nEntity:\\n    Crop\\n    id: integer\\n    name: string\\n    variety: string\\n    planting_date: datetime\\n    expected_harvest: datetime\\n    growth_stage: string\\n    field_id: integer\\n\\nEntity:\\n    SensorReading\\n    id: integer\\n    field_id: integer\\n    sensor_type: string\\n    value: float\\n    unit: string\\n    timestamp: datetime\\n\\nEntity:\\n    IrrigationZone\\n    id: integer\\n    field_id: integer\\n    name: string\\n    flow_rate: float\\n    is_active: boolean\\n    last_activated: datetime\\n\\nEntity:\\n    Harvest\\n    id: integer\\n    crop_id: integer\\n    yield_amount: float\\n    quality_grade: string\\n    harvest_date: datetime\\n\\nBehavior:\\n    PlantCrop\\n    Input: field_id integer, crop_name string, variety string, planting_date datetime\\n    Output: crop Crop\\n    Action: Register crop planting in field and schedule growth milestones\\n\\nBehavior:\\n    MonitorConditions\\n    Input: field_id integer\\n    Output: conditions dict\\n    Action: Aggregate sensor readings for soil moisture, temperature, and humidity\\n\\nBehavior:\\n    ActivateIrrigation\\n    Input: zone_id integer, duration_minutes integer\\n    Output: activation dict\\n    Action: Turn on irrigation zone for specified duration\\n\\nBehavior:\\n    RecordHarvest\\n    Input: crop_id integer, yield_amount float, quality string\\n    Output: harvest Harvest\\n    Action: Log harvest results and update field status\\n\\nBehavior:\\n    PredictYield\\n    Input: crop_id integer, conditions dict\\n    Output: prediction dict\\n    Action: Forecast expected yield based on conditions and growth stage\\n\\nCondition:\\n    When: soil moisture drops below 30 percent\\n    Then: Activate irrigation for affected zone for 30 minutes\\n\\nCondition:\\n    When: frost warning issued for area\\n    Then: Activate all irrigation zones for frost protection\\n\\nCondition:\\n    When: pest detection threshold exceeded in zone\\n    Then: Alert agronomist and recommend targeted treatment\\n\\nEvent:\\n    On: SensorReadingReceived\\n    Do: Update field dashboard and check irrigation thresholds\\n\\nEvent:\\n    On: GrowthStageChanged\\n    Do: Adjust irrigation schedule and update nutrient plan\\n\\nEvent:\\n    On: WeatherAlertReceived\\n    Do: Evaluate crop risk and activate protective measures\\n\\nOptimize:\\n    Irrigation water usage\\n    Priority: High\\n\\nOptimize:\\n    Crop yield per hectare\\n    Priority: High\\n\\nLearn:\\n    yield_prediction\\n    Based: weather_history, soil_conditions, planting_date, variety, field_location\\n    Adapt: refine yield estimates based on actual vs predicted harvests\"}\n{\"id\": \"aicl_024\", \"domain\": \"chat_app\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a real-time chat application with users, messages, channels, encryption, and smart replies\", \"code\": \"# Real-Time Chat Application\\n# Levels 1-9: Messaging platform with security and AI\\n\\nGoal:\\nCreate a real-time chat application with user management, direct messaging, group channels, message encryption, file sharing, and AI-powered smart replies\\n\\nConstraint:\\nMessage delivery latency must be under 500 milliseconds\\n\\nConstraint:\\nEnd-to-end encryption for private messages\\n\\nRisk:\\nMessage delivery failure\\n\\nRecovery:\\nQueue message for retry and notify sender of delayed delivery\\n\\nRisk:\\nChannel spam or abuse\\n\\nRecovery:\\nRate-limit user and flag for moderator review\\n\\nRisk:\\nFile sharing malware\\n\\nRecovery:\\nScan file before delivery and quarantine if threat detected\\n\\nLayer:\\n    Users\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Status\\n\\nLayer:\\n    Messages\\n    SubLayer:\\n        Direct\\n    SubLayer:\\n        Channels\\n\\nLayer:\\n    Media\\n    SubLayer:\\n        Files\\n    SubLayer:\\n        Links\\n\\nLayer:\\n    Security\\n    SubLayer:\\n        Encryption\\n    SubLayer:\\n        Moderation\\n\\nValidation:\\nMessages are delivered in real-time\\n\\nValidation:\\nPrivate messages are end-to-end encrypted\\n\\nValidation:\\nFile sharing scans for threats\\n\\nEntity:\\n    User\\n    id: integer\\n    username: string\\n    display_name: string\\n    email: string\\n    status: string\\n    last_seen: datetime\\n    avatar_url: string\\n\\nEntity:\\n    Message\\n    id: integer\\n    sender_id: integer\\n    channel_id: integer\\n    content: string\\n    type: string\\n    created_at: datetime\\n    edited: boolean\\n    reactions: dict\\n\\nEntity:\\n    Channel\\n    id: integer\\n    name: string\\n    type: string\\n    members: list\\n    created_at: datetime\\n    topic: string\\n\\nEntity:\\n    FileAttachment\\n    id: integer\\n    message_id: integer\\n    filename: string\\n    url: string\\n    size: integer\\n    mime_type: string\\n    scanned: boolean\\n\\nEntity:\\n    ModerationAction\\n    id: integer\\n    target_user_id: integer\\n    moderator_id: integer\\n    action: string\\n    reason: string\\n    created_at: datetime\\n\\nBehavior:\\n    SendMessage\\n    Input: sender_id integer, channel_id integer, content string, type string\\n    Output: message Message\\n    Action: Create message, encrypt if private, and broadcast to recipients\\n\\nBehavior:\\n    CreateChannel\\n    Input: name string, type string, members list\\n    Output: channel Channel\\n    Action: Create channel and add initial members\\n\\nBehavior:\\n    JoinChannel\\n    Input: user_id integer, channel_id integer\\n    Output: membership dict\\n    Action: Add user to channel and broadcast join notification\\n\\nBehavior:\\n    ShareFile\\n    Input: sender_id integer, channel_id integer, file dict\\n    Output: attachment FileAttachment\\n    Action: Scan file, store, and share with message\\n\\nBehavior:\\n    ModerateUser\\n    Input: target_id integer, moderator_id integer, action string, reason string\\n    Output: moderation ModerationAction\\n    Action: Apply moderation action and notify user\\n\\nBehavior:\\n    GenerateSmartReply\\n    Input: message_id integer, context list\\n    Output: suggestions list\\n    Action: Suggest quick replies based on message context\\n\\nCondition:\\n    When: user sends more than 10 messages per second\\n    Then: Rate-limit user and warn about spam policy\\n\\nCondition:\\n    When: file scan detects malware\\n    Then: Block file delivery and quarantine, notify sender and channel admin\\n\\nCondition:\\n    When: channel has no activity for 90 days\\n    Then: Archive channel and notify members\\n\\nEvent:\\n    On: MessageSent\\n    Do: Deliver to recipients, update unread counts, and trigger smart reply\\n\\nEvent:\\n    On: UserJoined\\n    Do: Send welcome message and update member list\\n\\nEvent:\\n    On: FileShared\\n    Do: Scan file and deliver if safe\\n\\nParallel:\\n    Messages\\n    Media\\n\\nOptimize:\\n    Message delivery latency\\n    Priority: Critical\\n\\nOptimize:\\n    Search indexing speed\\n    Priority: High\\n\\nLearn:\\n    smart_reply\\n    Based: conversation_context, user_style, message_sentiment, response_patterns\\n    Adapt: personalize suggestions based on user acceptance rate\\n\\nEncrypt:\\n    message content for private channels\\n    file attachments for private channels\\n\\nProtect:\\n    User data from unauthorized access\\n    Messages from interception\\n    Files from malware\"}\n{\"id\": \"aicl_025\", \"domain\": \"employee_hr\", \"levels\": [1,2,3,4,5,7], \"description\": \"Build an HR management system with employees, departments, payroll, leave, and performance reviews\", \"code\": \"# HR Management System\\n# Levels 1-5, 7: Human resources platform\\n\\nGoal:\\nCreate an HR management system with employee records, department management, payroll processing, leave management, and performance reviews\\n\\nConstraint:\\nPayroll must be processed on the 1st and 15th of each month without fail\\n\\nConstraint:\\nLeave balance must be accurate to the hour\\n\\nRisk:\\nPayroll calculation error\\n\\nRecovery:\\nRecalculate from source data and flag discrepancy for HR review\\n\\nRisk:\\nEmployee data breach\\n\\nRecovery:\\nLock affected records, notify security team, and comply with breach notification laws\\n\\nRisk:\\nLeave approval conflict with team coverage\\n\\nRecovery:\\nDetect minimum staffing violation and suggest alternative dates\\n\\nLayer:\\n    Employees\\n    SubLayer:\\n        Records\\n    SubLayer:\\n        Documents\\n\\nLayer:\\n    Organization\\n    SubLayer:\\n        Departments\\n    SubLayer:\\n        Teams\\n\\nLayer:\\n    Compensation\\n    SubLayer:\\n        Payroll\\n    SubLayer:\\n        Benefits\\n\\nLayer:\\n    TimeOff\\n    SubLayer:\\n        Leave\\n    SubLayer:\\n        Attendance\\n\\nValidation:\\nEmployee records are complete and up-to-date\\n\\nValidation:\\nPayroll is calculated accurately including deductions\\n\\nValidation:\\nLeave balances are tracked correctly\\n\\nEntity:\\n    Employee\\n    id: integer\\n    name: string\\n    email: string\\n    department_id: integer\\n    position: string\\n    hire_date: datetime\\n    salary: float\\n    status: string\\n    manager_id: integer\\n\\nEntity:\\n    Department\\n    id: integer\\n    name: string\\n    head_id: integer\\n    budget: float\\n    headcount: integer\\n\\nEntity:\\n    PayrollRecord\\n    id: integer\\n    employee_id: integer\\n    period: string\\n    base_salary: float\\n    deductions: float\\n    bonus: float\\n    net_pay: float\\n    processed_at: datetime\\n\\nEntity:\\n    LeaveRequest\\n    id: integer\\n    employee_id: integer\\n    type: string\\n    start_date: datetime\\n    end_date: datetime\\n    status: string\\n    approver_id: integer\\n    reason: string\\n\\nEntity:\\n    PerformanceReview\\n    id: integer\\n    employee_id: integer\\n    reviewer_id: integer\\n    rating: float\\n    goals: list\\n    feedback: string\\n    period: string\\n    completed_at: datetime\\n\\nBehavior:\\n    OnboardEmployee\\n    Input: name string, email string, department_id integer, position string, salary float\\n    Output: employee Employee\\n    Action: Create employee record and initiate onboarding workflow\\n\\nBehavior:\\n    ProcessPayroll\\n    Input: period string, department_id integer\\n    Output: records list\\n    Action: Calculate salaries, deductions, and bonuses for all employees\\n\\nBehavior:\\n    RequestLeave\\n    Input: employee_id integer, type string, start_date datetime, end_date datetime, reason string\\n    Output: request LeaveRequest\\n    Action: Submit leave request and check team coverage\\n\\nBehavior:\\n    ApproveLeave\\n    Input: request_id integer, approver_id integer\\n    Output: updated_request LeaveRequest\\n    Action: Approve leave and update employee balance\\n\\nBehavior:\\n    SubmitReview\\n    Input: employee_id integer, reviewer_id integer, rating float, feedback string, goals list\\n    Output: review PerformanceReview\\n    Action: Record performance review and update employee goals\\n\\nCondition:\\n    When: employee has less than 2 days of leave remaining\\n    Then: Flag for manager awareness when new leave is requested\\n\\nCondition:\\n    When: department headcount exceeds budget allocation\\n    Then: Alert HR director and freeze new hiring\\n\\nCondition:\\n    When: payroll net_pay differs from previous period by more than 20 percent\\n    Then: Flag for manual review before processing\\n\\nEvent:\\n    On: EmployeeOnboarded\\n    Do: Create accounts, assign equipment, and schedule orientation\\n\\nEvent:\\n    On: PayrollProcessed\\n    Do: Send pay stubs to employees and update accounting records\\n\\nEvent:\\n    On: LeaveApproved\\n    Do: Update team calendar and notify coverage team\\n\\nOptimize:\\n    Payroll processing speed\\n    Priority: High\\n\\nOptimize:\\n    Leave balance accuracy\\n    Priority: High\"}\n{\"id\": \"aicl_026\", \"domain\": \"podcast_platform\", \"levels\": [1,2,3,4,5,7,8], \"description\": \"Build a podcast platform with shows, episodes, subscriptions, and content recommendations\", \"code\": \"# Podcast Platform\\n# Levels 1-5, 7-8: Audio content platform with ML\\n\\nGoal:\\nCreate a podcast platform with show management, episode publishing, subscription handling, playback, and AI-powered content recommendations\\n\\nConstraint:\\nNew episodes must be available to subscribers within 1 minute of publish\\n\\nConstraint:\\nPlayback must start within 3 seconds\\n\\nRisk:\\nAudio file corruption\\n\\nRecovery:\\nFall back to backup encoding and alert content team\\n\\nRisk:\\nSubscription renewal failure\\n\\nRecovery:\\nRetry payment and provide 3-day grace period\\n\\nRisk:\\nRecommendation algorithm bias\\n\\nRecovery:\\nInject 20 percent diverse content and monitor satisfaction metrics\\n\\nLayer:\\n    Content\\n    SubLayer:\\n        Shows\\n    SubLayer:\\n        Episodes\\n\\nLayer:\\n    Users\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Subscriptions\\n\\nLayer:\\n    Playback\\n    SubLayer:\\n        Streaming\\n    SubLayer:\\n        History\\n\\nValidation:\\nEpisodes are published and distributed correctly\\n\\nValidation:\\nPlayback works across all supported devices\\n\\nValidation:\\nRecommendations improve with listening history\\n\\nEntity:\\n    Show\\n    id: integer\\n    title: string\\n    host: string\\n    description: string\\n    category: string\\n    episode_count: integer\\n    subscriber_count: integer\\n    cover_url: string\\n\\nEntity:\\n    Episode\\n    id: integer\\n    show_id: integer\\n    title: string\\n    description: string\\n    duration: integer\\n    audio_url: string\\n    published_at: datetime\\n    play_count: integer\\n\\nEntity:\\n    User\\n    id: integer\\n    username: string\\n    email: string\\n    subscriptions: list\\n    listening_history: list\\n    created_at: datetime\\n\\nEntity:\\n    Subscription\\n    id: integer\\n    user_id: integer\\n    show_id: integer\\n    subscribed_at: datetime\\n    notifications_enabled: boolean\\n\\nEntity:\\n    PlayHistory\\n    id: integer\\n    user_id: integer\\n    episode_id: integer\\n    position: integer\\n    completed: boolean\\n    played_at: datetime\\n\\nBehavior:\\n    PublishEpisode\\n    Input: show_id integer, title string, description string, audio_url string\\n    Output: episode Episode\\n    Action: Publish episode and notify all subscribers\\n\\nBehavior:\\n    SubscribeToShow\\n    Input: user_id integer, show_id integer\\n    Output: subscription Subscription\\n    Action: Add show to user subscriptions and enable notifications\\n\\nBehavior:\\n    StreamEpisode\\n    Input: episode_id integer, user_id integer\\n    Output: stream_url string\\n    Action: Authenticate and return streaming URL with resume position\\n\\nBehavior:\\n    RecordPlayProgress\\n    Input: user_id integer, episode_id integer, position integer\\n    Output: history PlayHistory\\n    Action: Save playback position for resume capability\\n\\nBehavior:\\n    GetRecommendations\\n    Input: user_id integer, count integer\\n    Output: shows list\\n    Action: Suggest shows based on listening history and preferences\\n\\nCondition:\\n    When: episode audio file exceeds 500 MB\\n    Then: Apply additional compression and warn about quality reduction\\n\\nCondition:\\n    When: user has not listened in 30 days\\n    Then: Send re-engagement email with personalized recommendations\\n\\nEvent:\\n    On: EpisodePublished\\n    Do: Notify subscribers, update RSS feed, and index for search\\n\\nEvent:\\n    On: EpisodeCompleted\\n    Do: Mark as listened, update recommendations, and suggest next episode\\n\\nEvent:\\n    On: NewSubscriber\\n    Do: Update show subscriber count and send welcome message\\n\\nOptimize:\\n    Audio streaming latency\\n    Priority: Critical\\n\\nOptimize:\\n    Recommendation relevance\\n    Priority: High\\n\\nLearn:\\n    content_recommendation\\n    Based: listening_history, subscription_genre, completion_rate, listening_time\\n    Adapt: weight recommendation factors based on skip rate and subscription conversions\"}\n{\"id\": \"aicl_027\", \"domain\": \"car_rental\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a car rental system with fleet, reservations, payments, and vehicle tracking\", \"code\": \"# Car Rental System\\n# Levels 1-7: Fleet management with pricing\\n\\nGoal:\\nCreate a car rental system with fleet management, reservation booking, vehicle handover, return processing, dynamic pricing, and maintenance tracking\\n\\nConstraint:\\nVehicle availability must update in real-time\\n\\nConstraint:\\nReservation confirmation must be sent within 30 seconds\\n\\nRisk:\\nVehicle returned with damage\\n\\nRecovery:\\nDocument damage with photos, charge repair fee, and update vehicle condition\\n\\nRisk:\\nCustomer returns vehicle late\\n\\nRecovery:\\nCharge late fee and adjust next reservation start time\\n\\nRisk:\\nVehicle breakdown during rental\\n\\nRecovery:\\nDispatch roadside assistance and provide replacement vehicle\\n\\nLayer:\\n    Fleet\\n    SubLayer:\\n        Vehicles\\n    SubLayer:\\n        Maintenance\\n\\nLayer:\\n    Reservations\\n    SubLayer:\\n        Booking\\n    SubLayer:\\n        Pricing\\n\\nLayer:\\n    Operations\\n    SubLayer:\\n        Handover\\n    SubLayer:\\n        Return\\n\\nValidation:\\nVehicles can be reserved and tracked correctly\\n\\nValidation:\\nDynamic pricing adjusts to demand\\n\\nValidation:\\nMaintenance is scheduled proactively\\n\\nEntity:\\n    Vehicle\\n    id: integer\\n    make: string\\n    model: string\\n    year: integer\\n    license_plate: string\\n    category: string\\n    daily_rate: float\\n    mileage: integer\\n    status: string\\n    condition: string\\n\\nEntity:\\n    Reservation\\n    id: integer\\n    customer_id: integer\\n    vehicle_id: integer\\n    pickup_location: string\\n    dropoff_location: string\\n    start_date: datetime\\n    end_date: datetime\\n    total_cost: float\\n    status: string\\n\\nEntity:\\n    Customer\\n    id: integer\\n    name: string\\n    email: string\\n    phone: string\\n    license_number: string\\n    license_valid: boolean\\n\\nEntity:\\n    MaintenanceRecord\\n    id: integer\\n    vehicle_id: integer\\n    type: string\\n    description: string\\n    cost: float\\n    performed_at: datetime\\n    next_due_mileage: integer\\n\\nBehavior:\\n    ReserveVehicle\\n    Input: customer_id integer, vehicle_id integer, start_date datetime, end_date datetime\\n    Output: reservation Reservation\\n    Action: Verify availability and create reservation with pricing\\n\\nBehavior:\\n    HandoverVehicle\\n    Input: reservation_id integer, condition_report dict\\n    Output: handover dict\\n    Action: Record vehicle condition and confirm customer possession\\n\\nBehavior:\\n    ProcessReturn\\n    Input: reservation_id integer, return_condition dict, mileage integer\\n    Output: return_receipt dict\\n    Action: Inspect vehicle, calculate charges, and update availability\\n\\nBehavior:\\n    ScheduleMaintenance\\n    Input: vehicle_id integer, type string, description string\\n    Output: record MaintenanceRecord\\n    Action: Schedule maintenance and mark vehicle as unavailable\\n\\nBehavior:\\n    CalculatePricing\\n    Input: vehicle_category string, duration integer, season string, demand float\\n    Output: price float\\n    Action: Compute rental price with dynamic adjustments\\n\\nCondition:\\n    When: vehicle mileage approaches maintenance threshold\\n    Then: Schedule preventive maintenance before next rental\\n\\nCondition:\\n    When: peak season demand exceeds fleet capacity\\n    Then: Increase daily rates and offer upgrade alternatives\\n\\nCondition:\\n    When: customer license is expired\\n    Then: Block reservation and notify customer to renew license\\n\\nEvent:\\n    On: ReservationCreated\\n    Do: Send confirmation and block vehicle availability\\n\\nEvent:\\n    On: VehicleReturned\\n    Do: Process charges, update mileage, and check maintenance schedule\\n\\nEvent:\\n    On: MaintenanceDue\\n    Do: Remove vehicle from availability and schedule service appointment\\n\\nParallel:\\n    Fleet\\n    Reservations\\n\\nOptimize:\\n    Fleet utilization rate\\n    Priority: High\\n\\nOptimize:\\n    Dynamic pricing accuracy\\n    Priority: High\"}\n{\"id\": \"aicl_028\", \"domain\": \"voting_system\", \"levels\": [1,2,3,4,9], \"description\": \"Build a secure electronic voting system with voters, ballots, candidates, and audit trails\", \"code\": \"# Secure Electronic Voting System\\n# Levels 1-4, 9: Elections with maximum security\\n\\nGoal:\\nCreate a secure electronic voting system with voter verification, ballot casting, vote tallying, candidate management, and cryptographic audit trails\\n\\nConstraint:\\nVotes must be anonymous and unverifiable to any third party\\n\\nConstraint:\\nTally results must be cryptographically verifiable\\n\\nConstraint:\\nSystem must be available 99.99 percent during voting period\\n\\nRisk:\\nVoter identity linked to cast ballot\\n\\nRecovery:\\nImplement zero-knowledge proof verification and audit privacy breach\\n\\nRisk:\\nBallot stuffing or duplicate voting\\n\\nRecovery:\\nReject duplicate vote, log attempt, and alert election officials\\n\\nRisk:\\nTally manipulation\\n\\nRecovery:\\nRe-run tally from encrypted ballots and compare with published results\\n\\nLayer:\\n    Voters\\n    SubLayer:\\n        Registration\\n    SubLayer:\\n        Verification\\n\\nLayer:\\n    Ballots\\n    SubLayer:\\n        Casting\\n    SubLayer:\\n        Storage\\n\\nLayer:\\n    Results\\n    SubLayer:\\n        Tallying\\n    SubLayer:\\n        Audit\\n\\nValidation:\\nEach eligible voter can cast exactly one ballot\\n\\nValidation:\\nVote tally matches encrypted ballot count\\n\\nValidation:\\nSystem prevents double voting\\n\\nEntity:\\n    Voter\\n    id: integer\\n    name: string\\n    district: string\\n    is_verified: boolean\\n    has_voted: boolean\\n\\nEntity:\\n    Candidate\\n    id: integer\\n    name: string\\n    party: string\\n    position: string\\n    vote_count: integer\\n\\nEntity:\\n    Ballot\\n    id: integer\\n    election_id: integer\\n    selections: dict\\n    timestamp: datetime\\n    hash: string\\n\\nEntity:\\n    Election\\n    id: integer\\n    name: string\\n    start_time: datetime\\n    end_time: datetime\\n    status: string\\n    total_votes: integer\\n\\nBehavior:\\n    VerifyVoter\\n    Input: voter_id integer, verification_data dict\\n    Output: verified boolean\\n    Action: Verify voter identity without linking to ballot choices\\n\\nBehavior:\\n    CastBallot\\n    Input: voter_id integer, election_id integer, selections dict\\n    Output: confirmation dict\\n    Action: Encrypt and store ballot selections, mark voter as voted\\n\\nBehavior:\\n    TallyVotes\\n    Input: election_id integer\\n    Output: results dict\\n    Action: Decrypt and count all ballots for the election\\n\\nBehavior:\\n    VerifyTally\\n    Input: election_id integer, published_results dict\\n    Output: verified boolean\\n    Action: Cryptographically verify that tally matches encrypted ballots\\n\\nCondition:\\n    When: voter attempts to cast second ballot\\n    Then: Reject ballot and log security incident\\n\\nCondition:\\n    When: election end time is reached\\n    Then: Close ballot casting and begin tally process\\n\\nEvent:\\n    On: BallotCast\\n    Do: Store encrypted ballot and send anonymous confirmation to voter\\n\\nEvent:\\n    On: ElectionClosed\\n    Do: Begin vote tally and prepare audit trail\\n\\nEncrypt:\\n    ballot selections\\n    voter verification_data\\n\\nProtect:\\n    Ballot anonymity from deanonymization attacks\\n    Tally integrity from manipulation\\n    Voter records from unauthorized access\"}\n{\"id\": \"aicl_029\", \"domain\": \"laundry_service\", \"levels\": [1,2,3,4], \"description\": \"Build a laundry service with orders, pickup, cleaning, and delivery\", \"code\": \"# Laundry Service Platform\\n# Levels 1-4: Simple service management\\n\\nGoal:\\nCreate a laundry service platform with order management, pickup scheduling, cleaning tracking, and delivery coordination\\n\\nConstraint:\\nPickup must occur within 2 hours of order\\n\\nRisk:\\nGarment damaged during cleaning\\n\\nRecovery:\\nReimburse customer at declared value and improve process for item type\\n\\nRisk:\\nPickup or delivery delay\\n\\nRecovery:\\nNotify customer with updated ETA and offer discount on next order\\n\\nLayer:\\n    Orders\\n    SubLayer:\\n        Placement\\n    SubLayer:\\n        Tracking\\n\\nLayer:\\n    Service\\n    SubLayer:\\n        Cleaning\\n    SubLayer:\\n        Quality\\n\\nValidation:\\nOrders progress through all stages correctly\\n\\nValidation:\\nGarment care instructions are followed\\n\\nEntity:\\n    Order\\n    id: integer\\n    customer_id: integer\\n    items: list\\n    status: string\\n    total: float\\n    placed_at: datetime\\n    estimated_delivery: datetime\\n\\nEntity:\\n    Garment\\n    id: integer\\n    type: string\\n    fabric: string\\n    care_instructions: string\\n    declared_value: float\\n    status: string\\n\\nEntity:\\n    Customer\\n    id: integer\\n    name: string\\n    address: string\\n    phone: string\\n    preferences: dict\\n\\nBehavior:\\n    PlaceOrder\\n    Input: customer_id integer, items list, pickup_time datetime\\n    Output: order Order\\n    Action: Create order with items and schedule pickup\\n\\nBehavior:\\n    ProcessGarment\\n    Input: garment_id integer, process_type string\\n    Output: updated_garment Garment\\n    Action: Apply cleaning process according to care instructions\\n\\nBehavior:\\n    QualityCheck\\n    Input: order_id integer\\n    Output: passed boolean\\n    Action: Verify all items meet quality standards before delivery\\n\\nCondition:\\n    When: garment care instructions specify dry clean only\\n    Then: Route to dry cleaning process and skip standard wash\\n\\nEvent:\\n    On: OrderPlaced\\n    Do: Schedule pickup and send confirmation\\n\\nEvent:\\n    On: CleaningComplete\\n    Do: Run quality check and prepare for delivery\"}\n{\"id\": \"aicl_030\", \"domain\": \"conference_organizer\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a conference organizer with sessions, speakers, attendees, scheduling, and live streaming\", \"code\": \"# Conference Organizer Platform\\n# Levels 1-7: Event management with parallel tracks\\n\\nGoal:\\nCreate a conference organizer with session scheduling, speaker management, attendee registration, venue coordination, and live streaming\\n\\nConstraint:\\nSession scheduling must prevent speaker time conflicts\\n\\nConstraint:\\nLive stream must have less than 5 second delay\\n\\nRisk:\\nSpeaker cancels at last minute\\n\\nRecovery:\\nFind replacement speaker or convert to panel discussion\\n\\nRisk:\\nSession room over capacity\\n\\nRecovery:\\nOpen overflow room with live stream and monitor headcount\\n\\nRisk:\\nLive stream technical failure\\n\\nRecovery:\\nSwitch to backup stream server and notify virtual attendees\\n\\nLayer:\\n    Program\\n    SubLayer:\\n        Sessions\\n    SubLayer:\\n        Tracks\\n\\nLayer:\\n    People\\n    SubLayer:\\n        Speakers\\n    SubLayer:\\n        Attendees\\n\\nLayer:\\n    Venue\\n    SubLayer:\\n        Rooms\\n    SubLayer:\\n        Equipment\\n\\nLayer:\\n    Virtual\\n    SubLayer:\\n        Streaming\\n    SubLayer:\\n        Recordings\\n\\nValidation:\\nSessions are scheduled without speaker or room conflicts\\n\\nValidation:\\nAttendees can register and select sessions\\n\\nValidation:\\nLive streaming works reliably\\n\\nEntity:\\n    Session\\n    id: integer\\n    title: string\\n    speaker_id: integer\\n    room_id: integer\\n    track: string\\n    start_time: datetime\\n    duration: integer\\n    capacity: integer\\n    registered_count: integer\\n\\nEntity:\\n    Speaker\\n    id: integer\\n    name: string\\n    bio: string\\n    company: string\\n    sessions: list\\n\\nEntity:\\n    Attendee\\n    id: integer\\n    name: string\\n    email: string\\n    ticket_type: string\\n    selected_sessions: list\\n\\nEntity:\\n    Room\\n    id: integer\\n    name: string\\n    capacity: integer\\n    equipment: list\\n    is_available: boolean\\n\\nEntity:\\n    Stream\\n    id: integer\\n    session_id: integer\\n    url: string\\n    viewer_count: integer\\n    status: string\\n\\nBehavior:\\n    ScheduleSession\\n    Input: title string, speaker_id integer, room_id integer, start_time datetime, duration integer\\n    Output: session Session\\n    Action: Check conflicts and create session in schedule\\n\\nBehavior:\\n    RegisterAttendee\\n    Input: name string, email string, ticket_type string\\n    Output: attendee Attendee\\n    Action: Register attendee and send confirmation\\n\\nBehavior:\\n    SelectSession\\n    Input: attendee_id integer, session_id integer\\n    Output: selection dict\\n    Action: Add session to attendee schedule if capacity allows\\n\\nBehavior:\\n    StartStream\\n    Input: session_id integer\\n    Output: stream Stream\\n    Action: Initialize live stream for session\\n\\nBehavior:\\n    RecordSession\\n    Input: session_id integer, recording_url string\\n    Output: recording dict\\n    Action: Save session recording for on-demand access\\n\\nCondition:\\n    When: session registered_count reaches capacity\\n    Then: Mark session as full and redirect to waitlist\\n\\nCondition:\\n    When: speaker has overlapping sessions\\n    Then: Reject scheduling and suggest alternative time\\n\\nCondition:\\n    When: stream viewer_count exceeds plan limit\\n    Then: Upgrade stream capacity and notify operations team\\n\\nEvent:\\n    On: SessionStarted\\n    Do: Activate live stream, open room, and notify registered attendees\\n\\nEvent:\\n    On: SessionEnded\\n    Do: Stop stream, save recording, and collect feedback\\n\\nEvent:\\n    On: AttendeeRegistered\\n    Do: Send welcome package and session selection guide\\n\\nParallel:\\n    Program\\n    Virtual\\n\\nOptimize:\\n    Schedule conflict detection\\n    Priority: Critical\\n\\nOptimize:\\n    Stream latency\\n    Priority: High\"}\n{\"id\": \"aicl_031\", \"domain\": \"subscription_box\", \"levels\": [1,2,3,4,5,7], \"description\": \"Build a subscription box service with products, boxes, shipments, and customer preferences\", \"code\": \"# Subscription Box Service\\n# Levels 1-5, 7: Curated delivery platform\\n\\nGoal:\\nCreate a subscription box service with product curation, box assembly, shipment management, customer preferences, and billing\\n\\nConstraint:\\nBoxes must be shipped by the 5th of each month\\n\\nConstraint:\\nCustomer preferences must be applied to at least 80 percent of box contents\\n\\nRisk:\\nProduct out of stock for box assembly\\n\\nRecovery:\\nSubstitute with equivalent product and notify customer of change\\n\\nRisk:\\nShipment lost in transit\\n\\nRecovery:\\nReship box with expedited shipping and file carrier claim\\n\\nRisk:\\nCustomer cancellation spike after pricing change\\n\\nRecovery:\\nOffer retention discount and analyze cancellation feedback\\n\\nLayer:\\n    Products\\n    SubLayer:\\n        Catalog\\n    SubLayer:\\n        Inventory\\n\\nLayer:\\n    Boxes\\n    SubLayer:\\n        Curation\\n    SubLayer:\\n        Assembly\\n\\nLayer:\\n    Customers\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Preferences\\n\\nLayer:\\n    Shipping\\n    SubLayer:\\n        Fulfillment\\n    SubLayer:\\n        Tracking\\n\\nValidation:\\nBoxes are curated based on customer preferences\\n\\nValidation:\\nShipments are delivered on schedule\\n\\nValidation:\\nBilling is processed correctly each cycle\\n\\nEntity:\\n    Product\\n    id: integer\\n    name: string\\n    category: string\\n    description: string\\n    retail_price: float\\n    cost_price: float\\n    stock: integer\\n\\nEntity:\\n    Box\\n    id: integer\\n    month: string\\n    theme: string\\n    products: list\\n    retail_value: float\\n    subscription_price: float\\n\\nEntity:\\n    Customer\\n    id: integer\\n    name: string\\n    email: string\\n    tier: string\\n    preferences: dict\\n    allergies: list\\n    subscription_start: datetime\\n\\nEntity:\\n    Shipment\\n    id: integer\\n    box_id: integer\\n    customer_id: integer\\n    tracking_number: string\\n    status: string\\n    shipped_at: datetime\\n    delivered_at: datetime\\n\\nBehavior:\\n    CurateBox\\n    Input: customer_id integer, month string, preferences dict\\n    Output: box Box\\n    Action: Select products matching customer preferences and tier\\n\\nBehavior:\\n    AssembleBox\\n    Input: box_id integer, products list\\n    Output: assembly dict\\n    Action: Package products into box with presentation materials\\n\\nBehavior:\\n    ShipBox\\n    Input: box_id integer, customer_id integer\\n    Output: shipment Shipment\\n    Action: Create shipment label and dispatch to carrier\\n\\nBehavior:\\n    ProcessBilling\\n    Input: customer_id integer, amount float\\n    Output: receipt dict\\n    Action: Charge subscription fee and send receipt\\n\\nCondition:\\n    When: product stock falls below monthly demand forecast\\n    Then: Trigger reorder from supplier and prepare substitution list\\n\\nCondition:\\n    When: customer has allergy matching a product in box\\n    Then: Remove product and substitute with safe alternative\\n\\nEvent:\\n    On: BoxCurated\\n    Do: Send preview to customer and proceed to assembly\\n\\nEvent:\\n    On: BoxShipped\\n    Do: Send tracking information and estimated delivery date\\n\\nEvent:\\n    On: BoxDelivered\\n    Do: Request feedback and product ratings\\n\\nOptimize:\\n    Curation personalization\\n    Priority: High\\n\\nOptimize:\\n    Shipping cost efficiency\\n    Priority: Medium\"}\n{\"id\": \"aicl_032\", \"domain\": \"video_streaming\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a video streaming platform with content, watchlists, recommendations, and adaptive bitrate\", \"code\": \"# Video Streaming Platform\\n# Levels 1-8: Entertainment with ML recommendations\\n\\nGoal:\\nCreate a video streaming platform with content catalog, watchlist management, adaptive bitrate streaming, personalized recommendations, and subscription billing\\n\\nConstraint:\\nVideo playback must start within 3 seconds\\n\\nConstraint:\\nContent recommendations must update within 2 hours of viewing activity\\n\\nRisk:\\nContent licensing expires during active viewing\\n\\nRecovery:\\nGracefully end stream with notice and suggest similar content\\n\\nRisk:\\nServer overload during premiere events\\n\\nRecovery:\\nEnable CDN edge caching and queue new viewer connections\\n\\nRisk:\\nRecommendation algorithm creates echo chamber\\n\\nRecovery:\\nInject 20 percent diverse content outside primary preferences\\n\\nLayer:\\n    Content\\n    SubLayer:\\n        Catalog\\n    SubLayer:\\n        Metadata\\n\\nLayer:\\n    Playback\\n    SubLayer:\\n        Streaming\\n    SubLayer:\\n        AdaptiveBitrate\\n\\nLayer:\\n    Users\\n    SubLayer:\\n        Profiles\\n    SubLayer:\\n        Watchlists\\n\\nLayer:\\n    Recommendations\\n    SubLayer:\\n        Engine\\n    SubLayer:\\n        Training\\n\\nValidation:\\nVideos stream smoothly at adaptive quality\\n\\nValidation:\\nWatchlists persist across sessions\\n\\nValidation:\\nRecommendations reflect viewing history\\n\\nEntity:\\n    Content\\n    id: integer\\n    title: string\\n    type: string\\n    genre: string\\n    duration: integer\\n    rating: float\\n    release_year: integer\\n    description: string\\n\\nEntity:\\n    User\\n    id: integer\\n    username: string\\n    email: string\\n    subscription_tier: string\\n    viewing_history: list\\n    preferences: dict\\n\\nEntity:\\n    WatchlistItem\\n    id: integer\\n    user_id: integer\\n    content_id: integer\\n    added_at: datetime\\n    status: string\\n\\nEntity:\\n    ViewingSession\\n    id: integer\\n    user_id: integer\\n    content_id: integer\\n    progress_percent: float\\n    quality: string\\n    started_at: datetime\\n\\nEntity:\\n    Subscription\\n    id: integer\\n    user_id: integer\\n    plan: string\\n    price: float\\n    status: string\\n    billing_date: datetime\\n\\nBehavior:\\n    StreamContent\\n    Input: content_id integer, user_id integer, preferred_quality string\\n    Output: stream_url string\\n    Action: Authenticate and return adaptive bitrate stream URL\\n\\nBehavior:\\n    AddToWatchlist\\n    Input: user_id integer, content_id integer\\n    Output: item WatchlistItem\\n    Action: Add content to user watchlist\\n\\nBehavior:\\n    RecordProgress\\n    Input: session_id integer, progress_percent float, quality string\\n    Output: updated_session ViewingSession\\n    Action: Save viewing progress for resume capability\\n\\nBehavior:\\n    GenerateRecommendations\\n    Input: user_id integer, count integer\\n    Output: content list\\n    Action: Suggest content based on viewing history and preferences\\n\\nBehavior:\\n    ProcessSubscription\\n    Input: user_id integer, plan string, payment_method string\\n    Output: subscription Subscription\\n    Action: Create or update subscription and process payment\\n\\nCondition:\\n    When: user on basic plan tries to access 4K content\\n    Then: Offer upgrade to premium plan\\n\\nCondition:\\n    When: viewing progress reaches 90 percent\\n    Then: Mark as watched and update recommendation training data\\n\\nCondition:\\n    When: network bandwidth drops below threshold\\n    Then: Reduce stream quality to prevent buffering\\n\\nEvent:\\n    On: ContentStarted\\n    Do: Create viewing session and begin progress tracking\\n\\nEvent:\\n    On: ContentCompleted\\n    Do: Update history, refresh recommendations, and suggest next content\\n\\nEvent:\\n    On: SubscriptionRenewed\\n    Do: Extend access and send confirmation\\n\\nParallel:\\n    Playback\\n    Recommendations\\n\\nOptimize:\\n    Stream startup latency\\n    Priority: Critical\\n\\nOptimize:\\n    Recommendation accuracy\\n    Priority: High\\n\\nLearn:\\n    content_recommendation\\n    Based: viewing_history, genre_affinity, completion_rate, time_of_day, session_length\\n    Adapt: weight factors based on watch-through rate and explicit ratings\"}\n{\"id\": \"aicl_033\", \"domain\": \"warehouse_robotics\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a warehouse robotics system with robots, tasks, navigation, and fleet coordination\", \"code\": \"# Warehouse Robotics System\\n# Levels 1-8: Automated warehouse with ML path optimization\\n\\nGoal:\\nCreate a warehouse robotics system with robot fleet management, task assignment, navigation planning, collision avoidance, and predictive maintenance\\n\\nConstraint:\\nRobot task assignment must complete within 2 seconds\\n\\nConstraint:\\nNavigation paths must be collision-free\\n\\nRisk:\\nRobot collision in shared aisle\\n\\nRecovery:\\nEmergency stop both robots, recalculate paths, and resume with priority\\n\\nRisk:\\nRobot battery depletion during task\\n\\nRecovery:\\nRoute to nearest charging station and reassign task to another robot\\n\\nRisk:\\nNavigation sensor malfunction\\n\\nRecovery:\\nSwitch to backup sensor mode and flag for maintenance\\n\\nLayer:\\n    Fleet\\n    SubLayer:\\n        Robots\\n    SubLayer:\\n        Coordination\\n\\nLayer:\\n    Tasks\\n    SubLayer:\\n        Assignment\\n    SubLayer:\\n        Execution\\n\\nLayer:\\n    Navigation\\n    SubLayer:\\n        PathPlanning\\n    SubLayer:\\n        CollisionAvoidance\\n\\nLayer:\\n    Maintenance\\n    SubLayer:\\n        Monitoring\\n    SubLayer:\\n        Scheduling\\n\\nValidation:\\nRobots execute assigned tasks correctly\\n\\nValidation:\\nNavigation paths are collision-free\\n\\nValidation:\\nFleet is coordinated without conflicts\\n\\nEntity:\\n    Robot\\n    id: integer\\n    name: string\\n    type: string\\n    battery_level: float\\n    location: dict\\n    status: string\\n    current_task_id: integer\\n    last_maintenance: datetime\\n\\nEntity:\\n    Task\\n    id: integer\\n    type: string\\n    source_location: dict\\n    destination_location: dict\\n    priority: integer\\n    assigned_robot_id: integer\\n    status: string\\n    created_at: datetime\\n\\nEntity:\\n    Path\\n    id: integer\\n    robot_id: integer\\n    waypoints: list\\n    estimated_duration: integer\\n    collision_risk: float\\n\\nEntity:\\n    Zone\\n    id: integer\\n    name: string\\n    type: string\\n    capacity: integer\\n    current_occupancy: integer\\n    restricted: boolean\\n\\nEntity:\\n    MaintenanceLog\\n    id: integer\\n    robot_id: integer\\n    type: string\\n    description: string\\n    performed_at: datetime\\n    next_due_at: datetime\\n\\nBehavior:\\n    AssignTask\\n    Input: task Task, available_robots list\\n    Output: assigned_robot Robot\\n    Action: Find best available robot based on location, battery, and capacity\\n\\nBehavior:\\n    PlanPath\\n    Input: robot_id integer, source dict, destination dict\\n    Output: path Path\\n    Action: Calculate optimal path avoiding obstacles and other robots\\n\\nBehavior:\\n    ExecuteTask\\n    Input: robot_id integer, task_id integer\\n    Output: result dict\\n    Action: Navigate robot through path and perform task\\n\\nBehavior:\\n    MonitorBattery\\n    Input: robot_id integer\\n    Output: status dict\\n    Action: Check battery level and schedule charging if needed\\n\\nBehavior:\\n    CoordinateFleet\\n    Input: active_robots list, pending_tasks list\\n    Output: assignments dict\\n    Action: Optimize task distribution across fleet\\n\\nBehavior:\\n    DetectCollision\\n    Input: paths list\\n    Output: conflicts list\\n    Action: Identify potential collisions between planned paths\\n\\nCondition:\\n    When: robot battery falls below 20 percent\\n    Then: Route to charging station and reassign current task\\n\\nCondition:\\n    When: two paths intersect at same time\\n    Then: Apply priority-based right-of-way and recalculate lower priority path\\n\\nCondition:\\n    When: zone occupancy reaches capacity\\n    Then: Queue robots at zone entry and redirect to alternative zones\\n\\nEvent:\\n    On: TaskCompleted\\n    Do: Update robot availability and check pending task queue\\n\\nEvent:\\n    On: CollisionDetected\\n    Do: Emergency stop affected robots and recalculate paths\\n\\nEvent:\\n    On: BatteryLow\\n    Do: Route robot to charging station and reassign task\\n\\nParallel:\\n    Fleet\\n    Navigation\\n\\nOptimize:\\n    Path planning speed\\n    Priority: Critical\\n\\nOptimize:\\n    Fleet utilization rate\\n    Priority: High\\n\\nLearn:\\n    path_optimization\\n    Based: warehouse_layout, traffic_patterns, time_of_day, task_frequency\\n    Adapt: pre-compute popular paths and adjust timing based on congestion patterns\"}\n{\"id\": \"aicl_034\", \"domain\": \"insurance_claims\", \"levels\": [1,2,3,4,5,7,9], \"description\": \"Build an insurance claims processing system with policies, claims, assessments, and payouts\", \"code\": \"# Insurance Claims Processing\\n# Levels 1-5, 7, 9: Claims with security and optimization\\n\\nGoal:\\nCreate an insurance claims processing system with policy management, claim submission, damage assessment, payout processing, and fraud detection\\n\\nConstraint:\\nClaims must be acknowledged within 24 hours\\n\\nConstraint:\\nPayout for approved claims must be processed within 5 business days\\n\\nRisk:\\nFraudulent claim submitted\\n\\nRecovery:\\nFlag for investigation and suspend payout pending review\\n\\nRisk:\\nClaim assessment delay beyond policy SLA\\n\\nRecovery:\\nEscalate to senior assessor and notify policyholder of extended timeline\\n\\nRisk:\\nPolicyholder data breach\\n\\nRecovery:\\nLock account, notify affected parties, and comply with breach disclosure laws\\n\\nLayer:\\n    Policies\\n    SubLayer:\\n        Management\\n    SubLayer:\\n        Coverage\\n\\nLayer:\\n    Claims\\n    SubLayer:\\n        Submission\\n    SubLayer:\\n        Assessment\\n\\nLayer:\\n    Payouts\\n    SubLayer:\\n        Processing\\n    SubLayer:\\n        Fraud\\n\\nValidation:\\nClaims are processed within SLA\\n\\nValidation:\\nFraud detection identifies suspicious claims\\n\\nEntity:\\n    Policy\\n    id: integer\\n    holder_id: integer\\n    type: string\\n    coverage_amount: float\\n    premium: float\\n    deductible: float\\n    start_date: datetime\\n    end_date: datetime\\n    status: string\\n\\nEntity:\\n    Claim\\n    id: integer\\n    policy_id: integer\\n    type: string\\n    description: string\\n    amount: float\\n    evidence: list\\n    status: string\\n    submitted_at: datetime\\n\\nEntity:\\n    Assessment\\n    id: integer\\n    claim_id: integer\\n    assessor_id: integer\\n    approved_amount: float\\n    notes: string\\n    completed_at: datetime\\n\\nEntity:\\n    Payout\\n    id: integer\\n    claim_id: integer\\n    amount: float\\n    method: string\\n    status: string\\n    processed_at: datetime\\n\\nEntity:\\n    PolicyHolder\\n    id: integer\\n    name: string\\n    email: string\\n    phone: string\\n    policies: list\\n    claim_history: list\\n\\nBehavior:\\n    SubmitClaim\\n    Input: policy_id integer, type string, description string, amount float, evidence list\\n    Output: claim Claim\\n    Action: Create claim and acknowledge receipt to policyholder\\n\\nBehavior:\\n    AssessClaim\\n    Input: claim_id integer, assessor_id integer\\n    Output: assessment Assessment\\n    Action: Review evidence and determine approved payout amount\\n\\nBehavior:\\n    ProcessPayout\\n    Input: claim_id integer, amount float, method string\\n    Output: payout Payout\\n    Action: Execute payout and send confirmation to policyholder\\n\\nBehavior:\\n    DetectFraud\\n    Input: claim Claim, history list\\n    Output: risk_score float\\n    Action: Analyze claim against fraud indicators and historical patterns\\n\\nCondition:\\n    When: claim amount exceeds policy coverage limit\\n    Then: Cap payout at coverage limit and notify policyholder\\n\\nCondition:\\n    When: fraud risk score exceeds 70 percent\\n    Then: Flag for investigation and suspend automatic payout\\n\\nEvent:\\n    On: ClaimSubmitted\\n    Do: Acknowledge receipt and assign to assessor\\n\\nEvent:\\n    On: ClaimApproved\\n    Do: Initiate payout process and notify policyholder\\n\\nOptimize:\\n    Claims processing speed\\n    Priority: High\\n\\nEncrypt:\\n    policy holder personal data\\n    claim evidence documents\\n\\nProtect:\\n    Policyholder data from unauthorized access\\n    Claims from fraudulent modification\"}\n{\"id\": \"aicl_035\", \"domain\": \"meal_prep\", \"levels\": [1,2,3,4,5,8], \"description\": \"Build a meal prep and recipe platform with ingredients, recipes, meal plans, and nutritional tracking\", \"code\": \"# Meal Prep and Recipe Platform\\n# Levels 1-5, 8: Cooking with adaptive learning\\n\\nGoal:\\nCreate a meal prep and recipe platform with recipe management, ingredient tracking, automated meal planning, grocery list generation, and adaptive nutritional optimization\\n\\nConstraint:\\nMeal plans must meet daily nutritional targets within 10 percent\\n\\nConstraint:\\nGrocery lists must account for ingredient overlap across recipes\\n\\nRisk:\\nRecipe contains undeclared allergen\\n\\nRecovery:\\nFlag recipe for review and alert users who saved it\\n\\nRisk:\\nNutritional calculation error\\n\\nRecovery:\\nRecalculate from ingredient database and update all affected meal plans\\n\\nLayer:\\n    Recipes\\n    SubLayer:\\n        Management\\n    SubLayer:\\n        Search\\n\\nLayer:\\n    Planning\\n    SubLayer:\\n        MealPlans\\n    SubLayer:\\n        GroceryLists\\n\\nLayer:\\n    Nutrition\\n    SubLayer:\\n        Tracking\\n    SubLayer:\\n        Optimization\\n\\nValidation:\\nRecipes have complete ingredient lists and instructions\\n\\nValidation:\\nMeal plans meet nutritional targets\\n\\nValidation:\\nGrocery lists are accurate and deduplicated\\n\\nEntity:\\n    Recipe\\n    id: integer\\n    title: string\\n    servings: integer\\n    prep_time: integer\\n    cook_time: integer\\n    ingredients: list\\n    instructions: list\\n    calories: integer\\n    macros: dict\\n    tags: list\\n\\nEntity:\\n    Ingredient\\n    id: integer\\n    name: string\\n    category: string\\n    unit: string\\n    calories_per_unit: float\\n    macros_per_unit: dict\\n    allergens: list\\n\\nEntity:\\n    MealPlan\\n    id: integer\\n    user_id: integer\\n    week: string\\n    days: dict\\n    total_calories: integer\\n    total_macros: dict\\n\\nEntity:\\n    GroceryList\\n    id: integer\\n    meal_plan_id: integer\\n    items: list\\n    estimated_cost: float\\n\\nEntity:\\n    User\\n    id: integer\\n    name: string\\n    daily_calorie_target: integer\\n    macro_targets: dict\\n    allergies: list\\n    preferences: list\\n\\nBehavior:\\n    CreateRecipe\\n    Input: title string, ingredients list, instructions list, servings integer\\n    Output: recipe Recipe\\n    Action: Create recipe with auto-calculated nutrition from ingredients\\n\\nBehavior:\\n    GenerateMealPlan\\n    Input: user_id integer, week string, preferences list, target_calories integer\\n    Output: meal_plan MealPlan\\n    Action: Select recipes meeting nutritional targets and preferences\\n\\nBehavior:\\n    GenerateGroceryList\\n    Input: meal_plan_id integer\\n    Output: grocery_list GroceryList\\n    Action: Aggregate and deduplicate ingredients across all recipes\\n\\nBehavior:\\n    CalculateNutrition\\n    Input: ingredients list, servings integer\\n    Output: nutrition dict\\n    Action: Sum calories and macros from ingredient database\\n\\nBehavior:\\n    SubstituteIngredient\\n    Input: recipe_id integer, old_ingredient string, new_ingredient string\\n    Output: updated_recipe Recipe\\n    Action: Replace ingredient and recalculate nutrition\\n\\nCondition:\\n    When: recipe contains allergen matching user allergy list\\n    Then: Exclude recipe from meal plan and suggest safe alternative\\n\\nCondition:\\n    When: meal plan daily calories deviate more than 10 percent from target\\n    Then: Flag for rebalancing and suggest portion adjustments\\n\\nEvent:\\n    On: RecipeCreated\\n    Do: Index for search and calculate nutrition\\n\\nEvent:\\n    On: MealPlanGenerated\\n    Do: Generate grocery list and send to user\\n\\nLearn:\\n    taste_preference\\n    Based: recipe_ratings, meal_plan_selections, cuisine_frequency, cooking_history\\n    Adapt: weight recipe recommendations based on learned taste profile\\n\\nLearn:\\n    budget_optimization\\n    Based: grocery_prices, seasonal_availability, purchase_frequency\\n    Adapt: suggest cost-effective ingredient substitutions\"}\n{\"id\": \"aicl_036\", \"domain\": \"container_shipping\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a container shipping management system with containers, vessels, ports, and cargo tracking\", \"code\": \"# Container Shipping Management\\n# Levels 1-7: Maritime logistics platform\\n\\nGoal:\\nCreate a container shipping management system with vessel scheduling, container tracking, port operations, cargo management, and route optimization\\n\\nConstraint:\\nContainer tracking must update every 30 minutes\\n\\nConstraint:\\nPort berth assignments must be conflict-free\\n\\nRisk:\\nContainer lost or damaged at sea\\n\\nRecovery:\\nFile insurance claim, notify cargo owner, and initiate recovery procedure\\n\\nRisk:\\nPort congestion causes vessel delays\\n\\nRecovery:\\nReroute to alternative port and adjust downstream schedule\\n\\nRisk:\\nHazardous cargo incident\\n\\nRecovery:\\nActivate emergency protocol and notify port authority\\n\\nLayer:\\n    Vessels\\n    SubLayer:\\n        Fleet\\n    SubLayer:\\n        Schedule\\n\\nLayer:\\n    Containers\\n    SubLayer:\\n        Tracking\\n    SubLayer:\\n        Allocation\\n\\nLayer:\\n    Ports\\n    SubLayer:\\n        Operations\\n    SubLayer:\\n        Berths\\n\\nLayer:\\n    Cargo\\n    SubLayer:\\n        Booking\\n    SubLayer:\\n        Documentation\\n\\nValidation:\\nContainers are tracked from loading to unloading\\n\\nValidation:\\nPort operations are scheduled without conflicts\\n\\nValidation:\\nCargo documentation is complete for customs\\n\\nEntity:\\n    Vessel\\n    id: integer\\n    name: string\\n    capacity_teu: integer\\n    current_voyage_id: integer\\n    status: string\\n    speed_knots: float\\n    flag: string\\n\\nEntity:\\n    Container\\n    id: integer\\n    number: string\\n    size: string\\n    type: string\\n    vessel_id: integer\\n    port_id: integer\\n    status: string\\n    cargo_type: string\\n    weight_kg: float\\n\\nEntity:\\n    Port\\n    id: integer\\n    name: string\\n    country: string\\n    berth_count: integer\\n    crane_count: integer\\n    capacity_teu: integer\\n\\nEntity:\\n    Voyage\\n    id: integer\\n    vessel_id: integer\\n    origin_port_id: integer\\n    destination_port_id: integer\\n    departure: datetime\\n    eta: datetime\\n    status: string\\n    containers: list\\n\\nEntity:\\n    CargoBooking\\n    id: integer\\n    container_id: integer\\n    shipper_id: integer\\n    commodity: string\\n    weight_kg: float\\n    value: float\\n    hazardous: boolean\\n    status: string\\n\\nBehavior:\\n    ScheduleVoyage\\n    Input: vessel_id integer, origin_port_id integer, destination_port_id integer, departure datetime\\n    Output: voyage Voyage\\n    Action: Create voyage and assign available containers\\n\\nBehavior:\\n    TrackContainer\\n    Input: container_id integer\\n    Output: location dict\\n    Action: Retrieve current location and status of container\\n\\nBehavior:\\n    AssignBerth\\n    Input: vessel_id integer, port_id integer, arrival datetime\\n    Output: berth dict\\n    Action: Find available berth and create port call schedule\\n\\nBehavior:\\n    BookCargo\\n    Input: shipper_id integer, commodity string, weight float, hazardous boolean\\n    Output: booking CargoBooking\\n    Action: Reserve container space and generate shipping documents\\n\\nBehavior:\\n    OptimizeRoute\\n    Input: origin_port_id integer, destination_port_id integer, weather dict\\n    Output: route dict\\n    Action: Calculate optimal route considering weather and fuel efficiency\\n\\nCondition:\\n    When: vessel ETA delayed by more than 24 hours\\n    Then: Notify all cargo owners and adjust downstream port schedules\\n\\nCondition:\\n    When: hazardous cargo requires special handling\\n    Then: Verify compliance documentation and assign designated berth\\n\\nEvent:\\n    On: VesselArrived\\n    Do: Initiate berth assignment and container unloading plan\\n\\nEvent:\\n    On: ContainerUnloaded\\n    Do: Update tracking and notify cargo owner of availability\\n\\nEvent:\\n    On: VoyageCompleted\\n    Do: Close voyage record and update vessel maintenance schedule\\n\\nParallel:\\n    Vessels\\n    Containers\\n\\nOptimize:\\n    Route fuel efficiency\\n    Priority: High\\n\\nOptimize:\\n    Port turnaround time\\n    Priority: High\"}\n{\"id\": \"aicl_037\", \"domain\": \"online_auction\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build an online auction platform with listings, bids, timers, and escrow payments\", \"code\": \"# Online Auction Platform\\n# Levels 1-7: Bidding with concurrency and pricing\\n\\nGoal:\\nCreate an online auction platform with item listings, real-time bidding, auction timers, escrow payments, and dispute resolution\\n\\nConstraint:\\nBid processing must complete within 500 milliseconds\\n\\nConstraint:\\nAuction timer must be accurate to the second\\n\\nRisk:\\nSnipe bidding in last seconds\\n\\nRecovery:\\nExtend auction by 5 minutes when bid placed in final 30 seconds\\n\\nRisk:\\nWinning bidder defaults on payment\\n\\nRecovery:\\nOffer item to second-highest bidder and penalize defaulting bidder\\n\\nRisk:\\nConcurrent bid creates inconsistency\\n\\nRecovery:\\nResolve by timestamp priority and notify affected bidders\\n\\nLayer:\\n    Listings\\n    SubLayer:\\n        Items\\n    SubLayer:\\n        Categories\\n\\nLayer:\\n    Bidding\\n    SubLayer:\\n        Auctions\\n    SubLayer:\\n        Bids\\n\\nLayer:\\n    Payments\\n    SubLayer:\\n        Escrow\\n    SubLayer:\\n        Settlement\\n\\nValidation:\\nBids are processed in correct time order\\n\\nValidation:\\nWinning bidder is correctly determined\\n\\nValidation:\\nEscrow payments are held and released properly\\n\\nEntity:\\n    AuctionItem\\n    id: integer\\n    title: string\\n    description: string\\n    starting_price: float\\n    current_price: float\\n    seller_id: integer\\n    category: string\\n    condition: string\\n\\nEntity:\\n    Auction\\n    id: integer\\n    item_id: integer\\n    start_time: datetime\\n    end_time: datetime\\n    reserve_price: float\\n    status: string\\n    bid_count: integer\\n\\nEntity:\\n    Bid\\n    id: integer\\n    auction_id: integer\\n    bidder_id: integer\\n    amount: float\\n    timestamp: datetime\\n    is_winning: boolean\\n\\nEntity:\\n    Escrow\\n    id: integer\\n    auction_id: integer\\n    buyer_id: integer\\n    amount: float\\n    status: string\\n    created_at: datetime\\n\\nEntity:\\n    User\\n    id: integer\\n    username: string\\n    email: string\\n    rating: float\\n    auctions_won: integer\\n\\nBehavior:\\n    ListAuctionItem\\n    Input: seller_id integer, title string, description string, starting_price float, duration_hours integer\\n    Output: auction Auction\\n    Action: Create auction listing with timer and reserve price\\n\\nBehavior:\\n    PlaceBid\\n    Input: auction_id integer, bidder_id integer, amount float\\n    Output: bid Bid\\n    Action: Validate bid amount and record with timestamp\\n\\nBehavior:\\n    CloseAuction\\n    Input: auction_id integer\\n    Output: result dict\\n    Action: Determine winner and initiate escrow payment\\n\\nBehavior:\\n    ProcessEscrow\\n    Input: auction_id integer, buyer_id integer, amount float\\n    Output: escrow Escrow\\n    Action: Hold payment in escrow until item delivery confirmed\\n\\nBehavior:\\n    ReleaseEscrow\\n    Input: escrow_id integer, confirmation dict\\n    Output: settlement dict\\n    Action: Release funds to seller after delivery confirmation\\n\\nCondition:\\n    When: bid placed within last 30 seconds of auction\\n    Then: Extend auction end time by 5 minutes\\n\\nCondition:\\n    When: winning bid is below reserve price\\n    Then: Notify seller and allow them to accept or reject\\n\\nCondition:\\n    When: bidder has unpaid items from previous auctions\\n    Then: Block new bids until outstanding payments are resolved\\n\\nEvent:\\n    On: BidPlaced\\n    Do: Update current price, notify previous highest bidder, and check auto-extend\\n\\nEvent:\\n    On: AuctionClosed\\n    Do: Determine winner and initiate escrow process\\n\\nEvent:\\n    On: DeliveryConfirmed\\n    Do: Release escrow funds to seller\\n\\nParallel:\\n    Bidding\\n    Payments\\n\\nOptimize:\\n    Bid processing latency\\n    Priority: Critical\\n\\nOptimize:\\n    Auction timer accuracy\\n    Priority: Critical\"}\n{\"id\": \"aicl_038\", \"domain\": \"calculator\", \"levels\": [1,2,3], \"description\": \"Build a simple calculator application\", \"code\": \"# Simple Calculator\\n# Levels 1-3: Basic arithmetic\\n\\nGoal:\\nCreate a calculator that performs basic arithmetic operations\\n\\nRisk:\\nDivision by zero\\n\\nRecovery:\\nReturn error message and prevent calculation\\n\\nRisk:\\nInput overflow beyond maximum value\\n\\nRecovery:\\nClamp result to maximum and warn user\\n\\nLayer:\\n    Calculation\\n\\nValidation:\\nAll arithmetic operations produce correct results\\n\\nEntity:\\n    Operation\\n    type: string\\n    operand_a: float\\n    operand_b: float\\n    result: float\\n\\nBehavior:\\n    Add\\n    Input: a float, b float\\n    Output: result float\\n    Action: Return sum of a and b\\n\\nBehavior:\\n    Subtract\\n    Input: a float, b float\\n    Output: result float\\n    Action: Return difference of a and b\\n\\nBehavior:\\n    Multiply\\n    Input: a float, b float\\n    Output: result float\\n    Action: Return product of a and b\\n\\nBehavior:\\n    Divide\\n    Input: a float, b float\\n    Output: result float\\n    Action: Return quotient of a divided by b\"}\n{\"id\": \"aicl_039\", \"domain\": \"todo_app\", \"levels\": [1,2,3,4], \"description\": \"Build a todo list application\", \"code\": \"# Todo List Application\\n# Levels 1-4: Task management\\n\\nGoal:\\nCreate a todo list application with task creation, completion, and priority management\\n\\nRisk:\\nTask deleted accidentally\\n\\nRecovery:\\nMove to trash and allow recovery for 30 days\\n\\nLayer:\\n    Tasks\\n    SubLayer:\\n        Active\\n    SubLayer:\\n        Completed\\n\\nValidation:\\nTasks can be created, completed, and deleted\\n\\nEntity:\\n    Task\\n    id: integer\\n    title: string\\n    description: string\\n    priority: string\\n    due_date: datetime\\n    completed: boolean\\n    created_at: datetime\\n\\nEntity:\\n    Category\\n    id: integer\\n    name: string\\n    color: string\\n    task_count: integer\\n\\nBehavior:\\n    CreateTask\\n    Input: title string, description string, priority string, due_date datetime\\n    Output: task Task\\n    Action: Create new task with specified attributes\\n\\nBehavior:\\n    CompleteTask\\n    Input: task_id integer\\n    Output: updated_task Task\\n    Action: Mark task as completed and move to completed list\\n\\nBehavior:\\n    DeleteTask\\n    Input: task_id integer\\n    Output: result dict\\n    Action: Move task to trash for recovery\\n\\nCondition:\\n    When: task due date has passed and task is not completed\\n    Then: Flag task as overdue and highlight in list\"}\n{\"id\": \"aicl_040\", \"domain\": \"temperature_converter\", \"levels\": [1,2,3], \"description\": \"Build a temperature converter between Celsius, Fahrenheit, and Kelvin\", \"code\": \"# Temperature Converter\\n# Levels 1-3: Simple unit conversion\\n\\nGoal:\\nCreate a temperature converter supporting Celsius, Fahrenheit, and Kelvin\\n\\nRisk:\\nTemperature below absolute zero\\n\\nRecovery:\\nClamp to absolute zero and warn user\\n\\nLayer:\\n    Conversion\\n\\nValidation:\\nConversions between all three scales are accurate\\n\\nEntity:\\n    Temperature\\n    value: float\\n    unit: string\\n\\nBehavior:\\n    CelsiusToFahrenheit\\n    Input: celsius float\\n    Output: fahrenheit float\\n    Action: Convert celsius to fahrenheit using formula\\n\\nBehavior:\\n    FahrenheitToCelsius\\n    Input: fahrenheit float\\n    Output: celsius float\\n    Action: Convert fahrenheit to celsius using formula\\n\\nBehavior:\\n    CelsiusToKelvin\\n    Input: celsius float\\n    Output: kelvin float\\n    Action: Convert celsius to kelvin by adding 273.15\"}\n{\"id\": \"aicl_041\", \"domain\": \"note_taking\", \"levels\": [1,2,3,4,5], \"description\": \"Build a note-taking application with folders, tags, search, and sharing\", \"code\": \"# Note Taking Application\\n# Levels 1-5: Notes with events\\n\\nGoal:\\nCreate a note-taking application with folders, tags, full-text search, version history, and note sharing\\n\\nConstraint:\\nSearch must return results within 500 milliseconds\\n\\nRisk:\\nNote accidentally deleted\\n\\nRecovery:\\nMove to trash and retain for 60 days with version history\\n\\nRisk:\\nSync conflict between devices\\n\\nRecovery:\\nCreate conflict copy and prompt user to merge\\n\\nLayer:\\n    Notes\\n    SubLayer:\\n        Content\\n    SubLayer:\\n        Versions\\n\\nLayer:\\n    Organization\\n    SubLayer:\\n        Folders\\n    SubLayer:\\n        Tags\\n\\nValidation:\\nNotes can be created, edited, and searched\\n\\nValidation:\\nVersion history is maintained accurately\\n\\nEntity:\\n    Note\\n    id: integer\\n    title: string\\n    content: string\\n    folder_id: integer\\n    tags: list\\n    created_at: datetime\\n    updated_at: datetime\\n    version: integer\\n\\nEntity:\\n    Folder\\n    id: integer\\n    name: string\\n    parent_id: integer\\n    note_count: integer\\n\\nEntity:\\n    Tag\\n    id: integer\\n    name: string\\n    color: string\\n    note_count: integer\\n\\nEntity:\\n    ShareLink\\n    id: integer\\n    note_id: integer\\n    token: string\\n    expires_at: datetime\\n    access_count: integer\\n\\nBehavior:\\n    CreateNote\\n    Input: title string, content string, folder_id integer, tags list\\n    Output: note Note\\n    Action: Create note in specified folder with tags\\n\\nBehavior:\\n    SearchNotes\\n    Input: query string, tags list, folder_id integer\\n    Output: notes list\\n    Action: Full-text search with tag and folder filters\\n\\nBehavior:\\n    ShareNote\\n    Input: note_id integer, expires_at datetime\\n    Output: link ShareLink\\n    Action: Generate shareable link with expiration\\n\\nCondition:\\n    When: note content exceeds 1 million characters\\n    Then: Warn about performance and suggest splitting into multiple notes\\n\\nEvent:\\n    On: NoteUpdated\\n    Do: Save version snapshot and update search index\\n\\nEvent:\\n    On: NoteShared\\n    Do: Track access count and notify owner on first view\"}\n{\"id\": \"aicl_042\", \"domain\": \"password_manager\", \"levels\": [1,2,3,4,9], \"description\": \"Build a password manager with vaults, entries, encryption, and auto-fill\", \"code\": \"# Password Manager\\n# Levels 1-4, 9: Security-focused credential storage\\n\\nGoal:\\nCreate a password manager with encrypted vaults, credential entries, password generation, auto-fill, and breach monitoring\\n\\nConstraint:\\nAll credentials must be encrypted with AES-256\\n\\nConstraint:\\nMaster password must never be stored in plain text\\n\\nRisk:\\nMaster password compromised\\n\\nRecovery:\\nForce credential re-encryption and notify user of security event\\n\\nRisk:\\nCredential breach detected in haveibeenpwned\\n\\nRecovery:\\nAlert user and prompt immediate password change for affected accounts\\n\\nLayer:\\n    Vault\\n    SubLayer:\\n        Entries\\n    SubLayer:\\n        Generation\\n\\nLayer:\\n    Security\\n    SubLayer:\\n        Encryption\\n    SubLayer:\\n        BreachMonitor\\n\\nValidation:\\nCredentials are stored encrypted and decrypted only on demand\\n\\nValidation:\\nPassword generator creates strong passwords\\n\\nEntity:\\n    Vault\\n    id: integer\\n    user_id: integer\\n    name: string\\n    entries_count: integer\\n    created_at: datetime\\n\\nEntity:\\n    Credential\\n    id: integer\\n    vault_id: integer\\n    service: string\\n    username: string\\n    password_encrypted: string\\n    url: string\\n    notes: string\\n    updated_at: datetime\\n\\nEntity:\\n    User\\n    id: integer\\n    email: string\\n    master_password_hash: string\\n    mfa_enabled: boolean\\n\\nBehavior:\\n    AddCredential\\n    Input: vault_id integer, service string, username string, password string, url string\\n    Output: credential Credential\\n    Action: Encrypt and store credential in vault\\n\\nBehavior:\\n    RetrieveCredential\\n    Input: credential_id integer, master_password string\\n    Output: decrypted dict\\n    Action: Decrypt and return credential for auto-fill\\n\\nBehavior:\\n    GeneratePassword\\n    Input: length integer, include_symbols boolean, include_numbers boolean\\n    Output: password string\\n    Action: Generate cryptographically random password\\n\\nBehavior:\\n    CheckBreach\\n    Input: credential_id integer\\n    Output: breached boolean\\n    Action: Check credential against known breach databases\\n\\nCondition:\\n    When: password is found in breach database\\n    Then: Alert user immediately and prompt password change\\n\\nCondition:\\n    When: vault is unlocked for more than 30 minutes\\n    Then: Auto-lock vault and require re-authentication\\n\\nEvent:\\n    On: CredentialAdded\\n    Do: Update vault index and check against breach database\\n\\nEvent:\\n    On: BreachDetected\\n    Do: Alert user and suggest password rotation for affected service\\n\\nEncrypt:\\n    credential password_encrypted\\n    credential notes\\n\\nProtect:\\n    Vault from unauthorized access\\n    Master password from extraction\\n    Credentials from interception\"}\n{\"id\": \"aicl_043\", \"domain\": \"blogging_platform\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a blogging platform with posts, authors, comments, SEO, and content recommendations\", \"code\": \"# Blogging Platform\\n# Levels 1-8: Content publishing with ML\\n\\nGoal:\\nCreate a blogging platform with post management, rich editing, SEO optimization, comment moderation, and content recommendations\\n\\nConstraint:\\nPage load time must be under 2 seconds\\n\\nConstraint:\\nSEO score must be calculated for every post\\n\\nRisk:\\nSpam comments flood blog\\n\\nRecovery:\\nAuto-moderate spam and enable CAPTCHA for flagged users\\n\\nRisk:\\nPost content loss during editing\\n\\nRecovery:\\nAuto-save draft every 30 seconds and restore from version history\\n\\nRisk:\\nSEO penalty from duplicate content\\n\\nRecovery:\\nDetect duplicates and canonicalize URLs\\n\\nLayer:\\n    Content\\n    SubLayer:\\n        Posts\\n    SubLayer:\\n        Drafts\\n\\nLayer:\\n    Engagement\\n    SubLayer:\\n        Comments\\n    SubLayer:\\n        Reactions\\n\\nLayer:\\n    SEO\\n    SubLayer:\\n        Analysis\\n    SubLayer:\\n        Optimization\\n\\nLayer:\\n    Discovery\\n    SubLayer:\\n        Recommendations\\n    SubLayer:\\n        RelatedPosts\\n\\nValidation:\\nPosts can be published, edited, and scheduled\\n\\nValidation:\\nSEO analysis provides actionable recommendations\\n\\nValidation:\\nComments are moderated effectively\\n\\nEntity:\\n    Post\\n    id: integer\\n    title: string\\n    slug: string\\n    content: string\\n    author_id: integer\\n    status: string\\n    seo_score: float\\n    published_at: datetime\\n    view_count: integer\\n    tags: list\\n\\nEntity:\\n    Author\\n    id: integer\\n    name: string\\n    bio: string\\n    avatar_url: string\\n    post_count: integer\\n    follower_count: integer\\n\\nEntity:\\n    Comment\\n    id: integer\\n    post_id: integer\\n    author_name: string\\n    content: string\\n    status: string\\n    created_at: datetime\\n\\nEntity:\\n    SEOAnalysis\\n    id: integer\\n    post_id: integer\\n    score: float\\n    issues: list\\n    suggestions: list\\n    keyword_density: dict\\n\\nBehavior:\\n    CreatePost\\n    Input: title string, content string, author_id integer, tags list\\n    Output: post Post\\n    Action: Create draft post with auto-generated slug\\n\\nBehavior:\\n    PublishPost\\n    Input: post_id integer, scheduled_at datetime\\n    Output: published_post Post\\n    Action: Run SEO analysis and publish or schedule post\\n\\nBehavior:\\n    AnalyzeSEO\\n    Input: post_id integer\\n    Output: analysis SEOAnalysis\\n    Action: Calculate SEO score and generate improvement suggestions\\n\\nBehavior:\\n    ModerateComment\\n    Input: comment_id integer, action string\\n    Output: result dict\\n    Action: Approve, reject, or flag comment\\n\\nBehavior:\\n    GetRecommendations\\n    Input: post_id integer, count integer\\n    Output: posts list\\n    Action: Suggest related posts based on content similarity\\n\\nCondition:\\n    When: post SEO score falls below 50\\n    Then: Block publication and show required improvements\\n\\nCondition:\\n    When: comment matches spam pattern\\n    Then: Auto-quarantine and require manual review\\n\\nEvent:\\n    On: PostPublished\\n    Do: Update sitemap, notify subscribers, and index for search\\n\\nEvent:\\n    On: CommentSubmitted\\n    Do: Run spam check and notify post author\\n\\nParallel:\\n    Content\\n    SEO\\n\\nOptimize:\\n    Page load speed\\n    Priority: Critical\\n\\nOptimize:\\n    SEO analysis speed\\n    Priority: High\\n\\nLearn:\\n    content_recommendation\\n    Based: reading_history, topic_similarity, engagement_metrics, author_affinity\\n    Adapt: weight recommendation factors based on click-through and time-on-page\"}

{\"id\": \"aicl_002\", \"domain\": \"healthcare\", \"levels\": [1,2,3,4,5,6,7,8,9,10], \"description\": \"Build a hospital management system with patient records, appointment scheduling, medical prescriptions, and billing\", \"code\": \"# Hospital Management System\\n# All 10 Levels: Complete healthcare platform\\n\\nGoal:\\nCreate a hospital management system with patient records, appointment scheduling, medical prescriptions, lab results, billing, and regulatory compliance\\n\\nConstraint:\\nMust comply with HIPAA privacy regulations\\n\\nConstraint:\\nPatient data must be encrypted at rest and in transit\\n\\nConstraint:\\nSystem must maintain 99.9% uptime for emergency access\\n\\nConstraint:\\nAll medical decisions must have audit trails\\n\\nRisk:\\nPatient data breach or unauthorized access\\n\\nRecovery:\\nImmediately revoke access, notify compliance officer, and log security incident\\n\\nRisk:\\nMedication interaction or allergic reaction\\n\\nRecovery:\\nAlert prescribing physician and pharmacist, flag patient record\\n\\nRisk:\\nAppointment double-booking\\n\\nRecovery:\\nDetect conflict and offer alternative time slots automatically\\n\\nRisk:\\nLab results lost or mismatched\\n\\nRecovery:\\nCross-reference patient ID and sample ID, flag discrepancy for review\\n\\nRisk:\\nBilling calculation error\\n\\nRecovery:\\nRecalculate from source data and flag for billing department review\\n\\nRisk:\\nSystem unavailable during emergency\\n\\nRecovery:\\nFailover to backup system within 30 seconds and use offline mode\\n\\nLayer:\\n PatientManagement\\n SubLayer:\\n Registration\\n SubLayer:\\n Records\\n\\nLayer:\\n Clinical\\n SubLayer:\\n Appointments\\n SubLayer:\\n Prescriptions\\n SubLayer:\\n LabResults\\n\\nLayer:\\n Billing\\n SubLayer:\\n Insurance\\n SubLayer:\\n Invoicing\\n\\nLayer:\\n Administration\\n SubLayer:\\n Staff\\n SubLayer:\\n Compliance\\n\\nValidation:\\nPatient records are accessible only by authorized medical staff\\n\\nValidation:\\nAppointments can be scheduled, rescheduled, and cancelled correctly\\n\\nValidation:\\nPrescriptions check for drug interactions before approval\\n\\nValidation:\\nLab results are matched to the correct patient\\n\\nValidation:\\nBilling accurately reflects services rendered\\n\\nValidation:\\nEmergency access is always available\\n\\nEntity:\\n Patient\\n id: integer\\n name: string\\n date_of_birth: datetime\\n insurance_id: string\\n medical_history: list\\n allergies: list\\n emergency_contact: dict\\n blood_type: string\\n\\nEntity:\\n Doctor\\n id: integer\\n name: string\\n specialty: string\\n license_number: string\\n schedule: dict\\n department: string\\n\\nEntity:\\n Appointment\\n id: integer\\n patient_id: integer\\n doctor_id: integer\\n datetime: datetime\\n duration: integer\\n status: string\\n reason: string\\n notes: string\\n\\nEntity:\\n Prescription\\n id: integer\\n patient_id: integer\\n doctor_id: integer\\n medications: list\\n date_issued: datetime\\n status: string\\n pharmacy_id: integer\\n\\nEntity:\\n LabResult\\n id: integer\\n patient_id: integer\\n test_name: string\\n result_value: float\\n unit: string\\n reference_range: string\\n status: string\\n collected_at: datetime\\n\\nEntity:\\n Invoice\\n id: integer\\n patient_id: integer\\n services: list\\n total_amount: float\\n insurance_coverage: float\\n patient_responsibility: float\\n status: string\\n issued_at: datetime\\n\\nEntity:\\n Department\\n id: integer\\n name: string\\n head_doctor_id: integer\\n capacity: integer\\n current_occupancy: integer\\n\\nBehavior:\\n RegisterPatient\\n Input: name string, dob datetime, insurance_id string\\n Output: patient Patient\\n Action: Create new patient record with demographic and insurance information\\n\\nBehavior:\\n ScheduleAppointment\\n Input: patient_id integer, doctor_id integer, preferred_time datetime, reason string\\n Output: appointment Appointment\\n Action: Find available slot and create appointment with conflict detection\\n\\nBehavior:\\n PrescribeMedication\\n Input: patient_id integer, doctor_id integer, medications list\\n Output: prescription Prescription\\n Action: Check drug interactions and allergies, then create prescription\\n\\nBehavior:\\n RecordLabResult\\n Input: patient_id integer, test_name string, result_value float, unit string\\n Output: lab_result LabResult\\n Action: Store lab result with reference range and flag abnormal values\\n\\nBehavior:\\n GenerateInvoice\\n Input: patient_id integer, services list, insurance_id string\\n Output: invoice Invoice\\n Action: Calculate costs, apply insurance coverage, generate invoice\\n\\nBehavior:\\n AdmitPatient\\n Input: patient_id integer, department_id integer, reason string\\n Output: admission dict\\n Action: Assign bed, create admission record, notify department staff\\n\\nBehavior:\\n DischargePatient\\n Input: patient_id integer, summary string, follow_up datetime\\n Output: discharge dict\\n Action: Generate discharge summary, schedule follow-up, release bed\\n\\nCondition:\\n When: prescription medications have known interaction\\n Then: Block prescription and alert prescribing doctor immediately\\n\\nCondition:\\n When: lab result is outside normal reference range\\n Then: Flag result as abnormal and notify attending physician\\n\\nCondition:\\n When: department occupancy reaches 90 percent\\n Then: Alert administration and activate overflow protocol\\n\\nCondition:\\n When: patient has outstanding unpaid invoices over 90 days\\n Then: Flag account for financial counseling\\n\\nEvent:\\n On: PatientAdmitted\\n Do: Assign bed, create chart, notify nursing staff, begin monitoring\\n\\nEvent:\\n On: LabResultReady\\n Do: Notify requesting doctor, update patient record, flag critical values\\n\\nEvent:\\n On: PrescriptionFilled\\n Do: Update medication record, log administration time\\n\\nEvent:\\n On: PatientDischarged\\n Do: Release bed, generate discharge summary, schedule follow-up, update billing\\n\\nParallel:\\n Clinical\\n Billing\\n\\nOptimize:\\n Appointment scheduling efficiency\\n Priority: High\\n\\nOptimize:\\n Emergency response time\\n Priority: Critical\\n\\nLearn:\\n no_show_prediction\\n Based: appointment history, weather, day_of_week, patient_age\\n Adapt: overbook_slots by 5 percent when no_show_probability exceeds 30 percent\\n\\nLearn:\\n readmission_risk\\n Based: diagnosis, length_of_stay, prior_admissions, social_factors\\n Adapt: schedule follow_up within 48 hours when risk_score exceeds 70 percent\\n\\nEncrypt:\\n patient medical_history\\n patient allergies\\n prescription medications\\n\\nProtect:\\n Patient records from unauthorized access\\n Medical decisions from tampering\\n Billing data from fraud\\n\\nNative:\\n hl7_interface\\n Language: Python\\n Code: \\\"import hl7; parser = hl7.parse(raw_message); return parser.extract_segments(['PID', 'PV1'])\\\"\"}

{\"id\": \"aicl_003\", \"domain\": \"social_media\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a social media platform with user profiles, posts, likes, comments, followers, news feed, and content moderation\", \"code\": \"# Social Media Platform\\n# Levels 1-9: Complete social network\\n\\nGoal:\\nCreate a social media platform with user profiles, posts, likes, comments, followers, personalized news feed, and AI-powered content moderation\\n\\nConstraint:\\nContent must be moderated within 5 minutes of posting\\n\\nConstraint:\\nFeed generation must complete within 200ms per user\\n\\nConstraint:\\nSystem must support 1 million concurrent users\\n\\nRisk:\\nInappropriate content posted before moderation\\n\\nRecovery:\\nFlag content for review and temporarily hide from public feed\\n\\nRisk:\\nFeed algorithm creates echo chambers\\n\\nRecovery:\\nInject diverse content and notify user of content diversity score\\n\\nRisk:\\nUser account compromised\\n\\nRecovery:\\nForce password reset, revoke sessions, and notify user via email\\n\\nRisk:\\nViral post overwhelms infrastructure\\n\\nRecovery:\\nEnable rate limiting and serve from cache\\n\\nLayer:\\n Users\\n SubLayer:\\n Profiles\\n SubLayer:\\n Authentication\\n\\nLayer:\\n Content\\n SubLayer:\\n Posts\\n SubLayer:\\n Comments\\n SubLayer:\\n Reactions\\n\\nLayer:\\n Social\\n SubLayer:\\n Followers\\n SubLayer:\\n Feed\\n\\nLayer:\\n Moderation\\n SubLayer:\\n Detection\\n SubLayer:\\n Review\\n\\nValidation:\\nUsers can create, edit, and delete their own posts\\n\\nValidation:\\nFeed displays relevant content from followed users\\n\\nValidation:\\nContent moderation catches violations within 5 minutes\\n\\nValidation:\\nFollower relationships are correctly maintained\\n\\nEntity:\\n User\\n id: integer\\n username: string\\n email: string\\n bio: string\\n avatar_url: string\\n is_private: boolean\\n created_at: datetime\\n follower_count: integer\\n following_count: integer\\n\\nEntity:\\n Post\\n id: integer\\n author_id: integer\\n content: string\\n media_urls: list\\n like_count: integer\\n comment_count: integer\\n created_at: datetime\\n visibility: string\\n\\nEntity:\\n Comment\\n id: integer\\n post_id: integer\\n author_id: integer\\n content: string\\n created_at: datetime\\n\\nEntity:\\n Follow\\n follower_id: integer\\n following_id: integer\\n created_at: datetime\\n status: string\\n\\nEntity:\\n Notification\\n id: integer\\n user_id: integer\\n type: string\\n content: string\\n read: boolean\\n created_at: datetime\\n\\nEntity:\\n ModerationFlag\\n id: integer\\n content_id: integer\\n content_type: string\\n reason: string\\n status: string\\n reviewer_id: integer\\n created_at: datetime\\n\\nBehavior:\\n CreatePost\\n Input: author_id integer, content string, media_urls list, visibility string\\n Output: post Post\\n Action: Create post and submit for moderation check\\n\\nBehavior:\\n LikePost\\n Input: user_id integer, post_id integer\\n Output: reaction dict\\n Action: Record like and update post like count\\n\\nBehavior:\\n AddComment\\n Input: post_id integer, author_id integer, content string\\n Output: comment Comment\\n Action: Create comment and notify post author\\n\\nBehavior:\\n FollowUser\\n Input: follower_id integer, following_id integer\\n Output: follow Follow\\n Action: Create follow relationship and generate notification\\n\\nBehavior:\\n GenerateFeed\\n Input: user_id integer, page integer\\n Output: posts list\\n Action: Aggregate posts from followed users ranked by relevance and recency\\n\\nBehavior:\\n ModerateContent\\n Input: content string, content_type string\\n Output: flag ModerationFlag\\n Action: Run content through moderation pipeline and flag violations\\n\\nBehavior:\\n SendNotification\\n Input: user_id integer, type string, content string\\n Output: notification Notification\\n Action: Create notification and push to user devices\\n\\nCondition:\\n When: user is private and receives follow request\\n Then: Queue request for user approval instead of auto-following\\n\\nCondition:\\n When: post receives more than 1000 likes in one hour\\n Then: Mark as trending and boost visibility\\n\\nCondition:\\n When: content matches harassment pattern\\n Then: Auto-hide content and escalate to human reviewer\\n\\nEvent:\\n On: PostCreated\\n Do: Add to followers feeds and run moderation check\\n\\nEvent:\\n On: UserFollowed\\n Do: Update follower counts and send notification to followed user\\n\\nEvent:\\n On: ContentFlagged\\n Do: Hide content from public view and notify content author\\n\\nEvent:\\n On: TrendingDetected\\n Do: Boost post distribution and add to trending section\\n\\nParallel:\\n Content\\n Moderation\\n\\nOptimize:\\n Feed generation latency\\n Priority: Critical\\n\\nOptimize:\\n Content moderation speed\\n Priority: High\\n\\nOptimize:\\n Notification delivery latency\\n Priority: Medium\\n\\nLearn:\\n feed_ranking\\n Based: engagement_history, post_recency, affinity_score, content_type\\n Adapt: weight factors based on user interaction patterns\\n\\nLearn:\\n toxicity_detection\\n Based: text_features, user_history, community_standards\\n Adapt: threshold based on false positive rate feedback\\n\\nEncrypt:\\n user email\\n user authentication_tokens\\n\\nProtect:\\n User data from unauthorized access\\n Content from manipulation\\n Feed algorithm from gaming\"}

{\"id\": \"aicl_004\", \"domain\": \"iot_smart_home\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build an IoT smart home system with device management, sensor monitoring, automation rules, and energy optimization\", \"code\": \"# IoT Smart Home System\\n# Levels 1-8: Connected home with automation and learning\\n\\nGoal:\\nCreate an IoT smart home system with device management, sensor monitoring, automation rules, energy optimization, and predictive maintenance\\n\\nConstraint:\\nDevice commands must execute within 500ms\\n\\nConstraint:\\nSystem must function offline for critical automations\\n\\nConstraint:\\nSupport up to 200 connected devices per home\\n\\nRisk:\\nDevice fails to respond to command\\n\\nRecovery:\\nRetry command 3 times with exponential backoff, then alert homeowner\\n\\nRisk:\\nSecurity breach through IoT device\\n\\nRecovery:\\nIsolate compromised device and notify security monitoring\\n\\nRisk:\\nAutomation rule conflicts\\n\\nRecovery:\\nDetect conflicts and request user clarification\\n\\nRisk:\\nPower outage disrupts system\\n\\nRecovery:\\nSwitch to battery backup for critical sensors and queue commands\\n\\nLayer:\\n Devices\\n SubLayer:\\n Discovery\\n SubLayer:\\n Control\\n\\nLayer:\\n Sensors\\n SubLayer:\\n Monitoring\\n SubLayer:\\n Alerts\\n\\nLayer:\\n Automation\\n SubLayer:\\n Rules\\n SubLayer:\\n Scenes\\n\\nLayer:\\n Energy\\n SubLayer:\\n Monitoring\\n SubLayer:\\n Optimization\\n\\nValidation:\\nDevices can be discovered, paired, and controlled\\n\\nValidation:\\nSensor readings are collected and threshold alerts fire correctly\\n\\nValidation:\\nAutomation rules execute in response to triggers\\n\\nValidation:\\nEnergy usage is tracked and optimized\\n\\nEntity:\\n Device\\n id: integer\\n name: string\\n type: string\\n room: string\\n status: string\\n firmware_version: string\\n last_seen: datetime\\n is_online: boolean\\n\\nEntity:\\n SensorReading\\n id: integer\\n device_id: integer\\n metric: string\\n value: float\\n unit: string\\n timestamp: datetime\\n\\nEntity:\\n AutomationRule\\n id: integer\\n name: string\\n trigger_condition: string\\n actions: list\\n is_active: boolean\\n priority: integer\\n\\nEntity:\\n Scene\\n id: integer\\n name: string\\n device_states: dict\\n schedule: string\\n\\nEntity:\\n EnergyUsage\\n id: integer\\n device_id: integer\\n consumption_kwh: float\\n period: string\\n timestamp: datetime\\n\\nEntity:\\n Alert\\n id: integer\\n device_id: integer\\n type: string\\n message: string\\n severity: string\\n acknowledged: boolean\\n created_at: datetime\\n\\nBehavior:\\n DiscoverDevice\\n Input: protocol string, timeout integer\\n Output: device Device\\n Action: Scan network for new devices and register them\\n\\nBehavior:\\n ControlDevice\\n Input: device_id integer, command string, params dict\\n Output: result dict\\n Action: Send command to device and confirm execution\\n\\nBehavior:\\n RecordReading\\n Input: device_id integer, metric string, value float, unit string\\n Output: reading SensorReading\\n Action: Store sensor reading and check threshold alerts\\n\\nBehavior:\\n ExecuteAutomation\\n Input: rule AutomationRule, trigger_data dict\\n Output: results list\\n Action: Evaluate rule conditions and execute actions in sequence\\n\\nBehavior:\\n ActivateScene\\n Input: scene_id integer\\n Output: results list\\n Action: Apply all device states defined in the scene\\n\\nBehavior:\\n OptimizeEnergy\\n Input: usage_history list, current_demand float\\n Output: recommendations list\\n Action: Analyze consumption patterns and suggest optimizations\\n\\nBehavior:\\n GenerateAlert\\n Input: device_id integer, type string, message string, severity string\\n Output: alert Alert\\n Action: Create alert and notify homeowner through preferred channel\\n\\nCondition:\\n When: temperature reading exceeds 30 degrees celsius\\n Then: Turn on air conditioning and send heat alert notification\\n\\nCondition:\\n When: motion detected at front door after midnight\\n Then: Turn on outdoor lights and send security alert\\n\\nCondition:\\n When: energy consumption exceeds daily budget\\n Then: Dim non-essential lights and postpone high-consumption automations\\n\\nCondition:\\n When: device offline for more than 10 minutes\\n Then: Mark device as disconnected and attempt reconnection\\n\\nEvent:\\n On: DeviceDiscovered\\n Do: Add to device registry and prompt user for room assignment\\n\\nEvent:\\n On: ThresholdBreached\\n Do: Generate alert and check automation rules for matching triggers\\n\\nEvent:\\n On: SceneActivated\\n Do: Log scene activation and update device states in UI\\n\\nEvent:\\n On: EnergyAnomalyDetected\\n Do: Generate efficiency report and suggest corrective actions\\n\\nParallel:\\n Sensors\\n Automation\\n\\nOptimize:\\n Device command latency\\n Priority: Critical\\n\\nOptimize:\\n Energy consumption efficiency\\n Priority: High\\n\\nLearn:\\n occupancy_pattern\\n Based: motion_sensors, time_of_day, day_of_week, historical_presence\\n Adapt: pre_heat_or_cool home 30 minutes before expected arrival\\n\\nLearn:\\n energy_forecast\\n Based: weather_forecast, historical_usage, occupancy_prediction\\n Adapt: adjust thermostat and schedule high-consumption devices off-peak\"}

{\"id\": \"aicl_005\", \"domain\": \"ride_sharing\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a ride-sharing platform with driver management, trip matching, dynamic pricing, and safety features\", \"code\": \"# Ride-Sharing Platform\\n# Levels 1-9: Complete ride-hailing with dynamic pricing and safety\\n\\nGoal:\\nCreate a ride-sharing platform with driver onboarding, real-time trip matching, dynamic pricing, GPS tracking, rating system, and safety features\\n\\nConstraint:\\nDriver matching must complete within 5 seconds\\n\\nConstraint:\\nGPS location updates every 3 seconds\\n\\nConstraint:\\nAll rides must have recording capability for dispute resolution\\n\\nRisk:\\nNo drivers available in high-demand area\\n\\nRecovery:\\nActivate surge pricing to attract drivers and notify waiting riders of estimated wait\\n\\nRisk:\\nDriver or rider safety incident\\n\\nRecovery:\\nTrigger emergency protocol, share live location with contacts, and contact authorities\\n\\nRisk:\\nGPS signal lost during trip\\n\\nRecovery:\\nSwitch to cell tower triangulation and estimate route from last known position\\n\\nRisk:\\nPayment dispute after trip\\n\\nRecovery:\\nHold payment in escrow and open dispute resolution process\\n\\nLayer:\\n Riders\\n SubLayer:\\n Profiles\\n SubLayer:\\n Requests\\n\\nLayer:\\n Drivers\\n SubLayer:\\n Onboarding\\n SubLayer:\\n Dispatch\\n\\nLayer:\\n Trips\\n SubLayer:\\n Matching\\n SubLayer:\\n Tracking\\n\\nLayer:\\n Pricing\\n SubLayer:\\n Calculation\\n SubLayer:\\n Settlement\\n\\nLayer:\\n Safety\\n SubLayer:\\n Monitoring\\n SubLayer:\\n Emergency\\n\\nValidation:\\nRiders can request and cancel rides\\n\\nValidation:\\nDrivers are matched to riders within 5 seconds\\n\\nValidation:\\nDynamic pricing reflects supply and demand\\n\\nValidation:\\nSafety features work during active trips\\n\\nValidation:\\nPayments are settled correctly after trip completion\\n\\nEntity:\\n Rider\\n id: integer\\n name: string\\n phone: string\\n email: string\\n rating: float\\n total_trips: integer\\n payment_method: string\\n\\nEntity:\\n Driver\\n id: integer\\n name: string\\n phone: string\\n vehicle_info: dict\\n license_number: string\\n rating: float\\n total_trips: integer\\n is_available: boolean\\n current_location: dict\\n\\nEntity:\\n Trip\\n id: integer\\n rider_id: integer\\n driver_id: integer\\n pickup_location: dict\\n dropoff_location: dict\\n status: string\\n fare: float\\n distance: float\\n duration: integer\\n started_at: datetime\\n completed_at: datetime\\n\\nEntity:\\n Fare\\n id: integer\\n trip_id: integer\\n base_fare: float\\n distance_fare: float\\n time_fare: float\\n surge_multiplier: float\\n total: float\\n currency: string\\n\\nEntity:\\n Rating\\n id: integer\\n trip_id: integer\\n reviewer_id: integer\\n reviewee_id: integer\\n score: integer\\n comment: string\\n created_at: datetime\\n\\nEntity:\\n SafetyAlert\\n id: integer\\n trip_id: integer\\n type: string\\n description: string\\n severity: string\\n resolved: boolean\\n created_at: datetime\\n\\nBehavior:\\n RequestRide\\n Input: rider_id integer, pickup dict, dropoff dict, ride_type string\\n Output: trip Trip\\n Action: Create ride request and search for available drivers\\n\\nBehavior:\\n MatchDriver\\n Input: trip Trip, available_drivers list\\n Output: matched_driver Driver\\n Action: Find nearest available driver with best rating and ETA\\n\\nBehavior:\\n CalculateFare\\n Input: distance float, duration integer, surge_multiplier float, ride_type string\\n Output: fare Fare\\n Action: Compute base, distance, time, and surge components\\n\\nBehavior:\\n TrackTrip\\n Input: trip_id integer, driver_location dict\\n Output: updated_trip Trip\\n Action: Update trip route and ETA based on live GPS data\\n\\nBehavior:\\n CompleteTrip\\n Input: trip_id integer, final_location dict\\n Output: completed_trip Trip\\n Action: Finalize fare, trigger payment, and request ratings\\n\\nBehavior:\\n RateParticipant\\n Input: trip_id integer, reviewer_id integer, reviewee_id integer, score integer, comment string\\n Output: rating Rating\\n Action: Record rating and update participant average\\n\\nBehavior:\\n TriggerEmergency\\n Input: trip_id integer, user_id integer, alert_type string\\n Output: alert SafetyAlert\\n Action: Contact authorities, share location, and activate safety protocol\\n\\nCondition:\\n When: demand exceeds available drivers by 50 percent\\n Then: Activate surge pricing and notify riders of increased fares\\n\\nCondition:\\n When: driver deviates from route by more than 500 meters\\n Then: Log deviation and check for safety alert trigger\\n\\nCondition:\\n When: trip duration exceeds estimated time by 40 percent\\n Then: Notify rider of delay and check for traffic issues\\n\\nEvent:\\n On: RideRequested\\n Do: Broadcast to nearby drivers and start 30-second matching window\\n\\nEvent:\\n On: DriverAccepted\\n Do: Notify rider with driver info and ETA, start tracking\\n\\nEvent:\\n On: TripCompleted\\n Do: Process payment, request mutual ratings, update driver availability\\n\\nEvent:\\n On: SafetyAlertTriggered\\n Do: Contact emergency services, share live location with trusted contacts\\n\\nParallel:\\n Trips\\n Pricing\\n\\nOptimize:\\n Driver matching speed\\n Priority: Critical\\n\\nOptimize:\\n Fare calculation accuracy\\n Priority: High\\n\\nLearn:\\n demand_prediction\\n Based: time_of_day, weather, events, historical_demand\\n Adapt: pre_position drivers in high-demand areas before predicted surge\\n\\nLearn:\\n eta_estimation\\n Based: route_data, traffic_patterns, weather_conditions, time_of_day\\n Adapt: adjust eta model based on actual vs predicted arrival times\\n\\nEncrypt:\\n rider phone\\n rider email\\n driver phone\\n\\nProtect:\\n Trip tracking data from unauthorized access\\n Payment information from fraud\\n Driver location history from stalking\"}

{\"id\": \"aicl_006\", \"domain\": \"online_education\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build an online education platform with courses, students, assignments, grading, and adaptive learning\", \"code\": \"# Online Education Platform\\n# Levels 1-8: Learning management with adaptive features\\n\\nGoal:\\nCreate an online education platform with course management, student enrollment, assignment submission, grading, progress tracking, and adaptive learning paths\\n\\nConstraint:\\nVideo streaming must support at least 10000 concurrent viewers\\n\\nConstraint:\\nAssignment submission deadline must be enforced to the second\\n\\nRisk:\\nStudent loses work during assignment submission\\n\\nRecovery:\\nAuto-save draft every 30 seconds and restore from last save on reconnect\\n\\nRisk:\\nPlagiarism detected in submitted work\\n\\nRecovery:\\nFlag submission for academic review and notify student of policy violation\\n\\nRisk:\\nCourse content inaccessible during exam\\n\\nRecovery:\\nSwitch to backup content server and extend exam deadline by lost time\\n\\nLayer:\\n Courses\\n SubLayer:\\n Content\\n SubLayer:\\n Schedule\\n\\nLayer:\\n Students\\n SubLayer:\\n Enrollment\\n SubLayer:\\n Progress\\n\\nLayer:\\n Assessment\\n SubLayer:\\n Assignments\\n SubLayer:\\n Grading\\n\\nValidation:\\nStudents can enroll in and drop courses within allowed periods\\n\\nValidation:\\nAssignments can be submitted and graded correctly\\n\\nValidation:\\nProgress tracking accurately reflects completion\\n\\nEntity:\\n Course\\n id: integer\\n title: string\\n description: string\\n instructor_id: integer\\n start_date: datetime\\n end_date: datetime\\n max_enrollment: integer\\n syllabus: list\\n difficulty: string\\n\\nEntity:\\n Student\\n id: integer\\n name: string\\n email: string\\n enrolled_courses: list\\n gpa: float\\n completed_courses: list\\n\\nEntity:\\n Assignment\\n id: integer\\n course_id: integer\\n title: string\\n description: string\\n due_date: datetime\\n max_points: float\\n type: string\\n\\nEntity:\\n Submission\\n id: integer\\n assignment_id: integer\\n student_id: integer\\n content: string\\n submitted_at: datetime\\n grade: float\\n feedback: string\\n status: string\\n\\nEntity:\\n Progress\\n id: integer\\n student_id: integer\\n course_id: integer\\n completion_percent: float\\n last_accessed: datetime\\n time_spent: integer\\n\\nBehavior:\\n EnrollStudent\\n Input: student_id integer, course_id integer\\n Output: enrollment dict\\n Action: Register student in course if capacity allows and prerequisites met\\n\\nBehavior:\\n SubmitAssignment\\n Input: assignment_id integer, student_id integer, content string\\n Output: submission Submission\\n Action: Record submission with timestamp and check against deadline\\n\\nBehavior:\\n GradeSubmission\\n Input: submission_id integer, grade float, feedback string\\n Output: graded_submission Submission\\n Action: Apply grade and feedback, update student progress\\n\\nBehavior:\\n TrackProgress\\n Input: student_id integer, course_id integer\\n Output: progress Progress\\n Action: Calculate completion percentage from submitted and graded work\\n\\nBehavior:\\n GenerateReport\\n Input: student_id integer, course_id integer\\n Output: report dict\\n Action: Compile grades, progress, and time spent into student report\\n\\nBehavior:\\n CheckPrerequisites\\n Input: student_id integer, course_id integer\\n Output: eligible boolean\\n Action: Verify student has completed all prerequisite courses\\n\\nCondition:\\n When: assignment submitted after deadline\\n Then: Mark as late and apply penalty according to course policy\\n\\nCondition:\\n When: student completion_percent drops below 30 percent at midpoint\\n Then: Flag student for intervention and notify academic advisor\\n\\nCondition:\\n When: course enrollment exceeds 90 percent capacity\\n Then: Create waitlist and notify prospective students\\n\\nEvent:\\n On: StudentEnrolled\\n Do: Send welcome email, create progress tracker, add to course roster\\n\\nEvent:\\n On: AssignmentGraded\\n Do: Update student progress, send grade notification, check completion criteria\\n\\nEvent:\\n On: CourseCompleted\\n Do: Issue certificate, update transcript, request course evaluation\\n\\nParallel:\\n Assessment\\n Students\\n\\nOptimize:\\n Video streaming quality\\n Priority: High\\n\\nOptimize:\\n Assignment grading turnaround\\n Priority: Medium\\n\\nLearn:\\n difficulty_adaptation\\n Based: student_performance, time_per_assignment, quiz_scores, engagement_metrics\\n Adapt: recommend supplementary content when struggling and advance when excelling\"}

{\"id\": \"aicl_007\", \"domain\": \"inventory_management\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build an inventory management system with warehouses, products, shipments, suppliers, and stock tracking\", \"code\": \"# Inventory Management System\\n# Levels 1-7: Warehouse and supply chain tracking\\n\\nGoal:\\nCreate an inventory management system with multi-warehouse tracking, product catalog, shipment management, supplier coordination, and automated reorder\\n\\nConstraint:\\nStock levels must update in real-time across all warehouses\\n\\nConstraint:\\nReorder alerts must trigger before stock reaches critical levels\\n\\nRisk:\\nStockout on critical products\\n\\nRecovery:\\nEmergency order from backup supplier and expedite shipping\\n\\nRisk:\\nWarehouse inventory count discrepancy\\n\\nRecovery:\\nFreeze stock movement and initiate cycle count audit\\n\\nRisk:\\nSupplier delivery delayed\\n\\nRecovery:\\nActivate alternative supplier and adjust production schedule\\n\\nLayer:\\n Products\\n SubLayer:\\n Catalog\\n SubLayer:\\n Categories\\n\\nLayer:\\n Warehouses\\n SubLayer:\\n Locations\\n SubLayer:\\n Stock\\n\\nLayer:\\n Supply\\n SubLayer:\\n Suppliers\\n SubLayer:\\n Orders\\n\\nLayer:\\n Shipping\\n SubLayer:\\n Outbound\\n SubLayer:\\n Receiving\\n\\nValidation:\\nProduct stock levels are accurate across all warehouses\\n\\nValidation:\\nReorder points trigger purchase orders automatically\\n\\nValidation:\\nShipments are tracked from dispatch to receipt\\n\\nEntity:\\n Product\\n id: integer\\n name: string\\n sku: string\\n category: string\\n unit_price: float\\n reorder_point: integer\\n reorder_quantity: integer\\n unit: string\\n\\nEntity:\\n Warehouse\\n id: integer\\n name: string\\n location: string\\n capacity: integer\\n manager_id: integer\\n\\nEntity:\\n StockLevel\\n product_id: integer\\n warehouse_id: integer\\n quantity: integer\\n reserved_quantity: integer\\n last_counted: datetime\\n\\nEntity:\\n Supplier\\n id: integer\\n name: string\\n contact_email: string\\n lead_time_days: integer\\n rating: float\\n products_supplied: list\\n\\nEntity:\\n PurchaseOrder\\n id: integer\\n supplier_id: integer\\n products: list\\n total_amount: float\\n status: string\\n order_date: datetime\\n expected_delivery: datetime\\n\\nEntity:\\n Shipment\\n id: integer\\n origin_id: integer\\n destination_id: integer\\n items: list\\n status: string\\n tracking_number: string\\n shipped_at: datetime\\n delivered_at: datetime\\n\\nBehavior:\\n AddProduct\\n Input: name string, sku string, category string, price float\\n Output: product Product\\n Action: Create product in catalog with reorder parameters\\n\\nBehavior:\\n UpdateStock\\n Input: product_id integer, warehouse_id integer, quantity_change integer\\n Output: stock StockLevel\\n Action: Adjust stock level and check reorder threshold\\n\\nBehavior:\\n CreatePurchaseOrder\\n Input: supplier_id integer, products list\\n Output: order PurchaseOrder\\n Action: Generate purchase order and send to supplier\\n\\nBehavior:\\n ReceiveShipment\\n Input: shipment_id integer, received_items list\\n Output: updated_stock list\\n Action: Verify received quantities and update stock levels\\n\\nBehavior:\\n TransferStock\\n Input: product_id integer, from_warehouse integer, to_warehouse integer, quantity integer\\n Output: shipment Shipment\\n Action: Create internal transfer shipment between warehouses\\n\\nBehavior:\\n CheckReorder\\n Input: product_id integer, warehouse_id integer\\n Output: needs_reorder boolean\\n Action: Compare current stock to reorder point and trigger if needed\\n\\nCondition:\\n When: stock level falls below reorder point\\n Then: Automatically generate purchase order to primary supplier\\n\\nCondition:\\n When: received shipment quantity does not match expected\\n Then: Log discrepancy and notify warehouse manager for investigation\\n\\nCondition:\\n When: warehouse capacity exceeds 95 percent\\n Then: Alert management and halt incoming shipments\\n\\nEvent:\\n On: StockUpdated\\n Do: Refresh inventory dashboard and check reorder thresholds\\n\\nEvent:\\n On: PurchaseOrderApproved\\n Do: Send order to supplier and schedule expected delivery\\n\\nEvent:\\n On: ShipmentDelivered\\n Do: Update stock levels and close shipment record\\n\\nParallel:\\n Products\\n Shipping\\n\\nOptimize:\\n Warehouse space utilization\\n Priority: High\\n\\nOptimize:\\n Reorder timing accuracy\\n Priority: High\"}

{\"id\": \"aicl_008\", \"domain\": \"flight_booking\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a flight booking system with flight search, reservation, check-in, boarding, and loyalty program\", \"code\": \"# Flight Booking System\\n# Levels 1-9: Airline reservation with loyalty and security\\n\\nGoal:\\nCreate a flight booking system with flight search, seat reservation, passenger check-in, boarding management, loyalty program, and aviation security compliance\\n\\nConstraint:\\nSearch results must return within 2 seconds\\n\\nConstraint:\\nMust comply with TSA security requirements\\n\\nConstraint:\\nOverbooking limited to 10 percent of capacity\\n\\nRisk:\\nFlight overbooking leads to denied boarding\\n\\nRecovery:\\nOffer compensation and rebook on next available flight\\n\\nRisk:\\nPassenger no-show on connecting flight\\n\\nRecovery:\\nAutomatically rebook on next flight and send notification\\n\\nRisk:\\nFlight cancellation due to weather\\n\\nRecovery:\\nRebook all passengers and provide accommodation vouchers\\n\\nRisk:\\nPayment processing failure during booking\\n\\nRecovery:\\nHold reservation for 30 minutes and retry payment\\n\\nLayer:\\n Flights\\n SubLayer:\\n Schedule\\n SubLayer:\\n Availability\\n\\nLayer:\\n Booking\\n SubLayer:\\n Search\\n SubLayer:\\n Reservation\\n\\nLayer:\\n CheckIn\\n SubLayer:\\n Online\\n SubLayer:\\n Airport\\n\\nLayer:\\n Loyalty\\n SubLayer:\\n Points\\n SubLayer:\\n Tiers\\n\\nValidation:\\nFlights can be searched by route, date, and passenger count\\n\\nValidation:\\nBookings are confirmed and tickets issued correctly\\n\\nValidation:\\nCheck-in process assigns seats and issues boarding passes\\n\\nValidation:\\nLoyalty points are earned and redeemed correctly\\n\\nEntity:\\n Flight\\n id: integer\\n flight_number: string\\n origin: string\\n destination: string\\n departure_time: datetime\\n arrival_time: datetime\\n aircraft_type: string\\n total_seats: integer\\n available_seats: integer\\n base_price: float\\n status: string\\n\\nEntity:\\n Passenger\\n id: integer\\n name: string\\n passport_number: string\\n nationality: string\\n date_of_birth: datetime\\n loyalty_id: string\\n special_needs: list\\n\\nEntity:\\n Reservation\\n id: integer\\n flight_id: integer\\n passenger_id: integer\\n seat_number: string\\n class: string\\n price_paid: float\\n status: string\\n booked_at: datetime\\n\\nEntity:\\n BoardingPass\\n id: integer\\n reservation_id: integer\\n gate: string\\n boarding_time: datetime\\n zone: string\\n issued_at: datetime\\n\\nEntity:\\n LoyaltyAccount\\n id: integer\\n passenger_id: integer\\n tier: string\\n points_balance: integer\\n lifetime_points: integer\\n\\nBehavior:\\n SearchFlights\\n Input: origin string, destination string, date datetime, passengers integer\\n Output: flights list\\n Action: Find matching flights with available seats and pricing\\n\\nBehavior:\\n BookFlight\\n Input: flight_id integer, passenger_id integer, seat_class string\\n Output: reservation Reservation\\n Action: Reserve seat, process payment, and issue confirmation\\n\\nBehavior:\\n CheckIn\\n Input: reservation_id integer, bags integer\\n Output: boarding_pass BoardingPass\\n Action: Confirm passenger identity, assign seat, and issue boarding pass\\n\\nBehavior:\\n EarnPoints\\n Input: passenger_id integer, flight_distance integer, class string\\n Output: updated_account LoyaltyAccount\\n Action: Calculate and add loyalty points based on distance and class\\n\\nBehavior:\\n RedeemPoints\\n Input: account_id integer, points integer, reward_type string\\n Output: redemption dict\\n Action: Deduct points and issue reward or upgrade\\n\\nBehavior:\\n CancelReservation\\n Input: reservation_id integer, reason string\\n Output: refund dict\\n Action: Release seat, process refund according to fare rules\\n\\nCondition:\\n When: flight is over 80 percent full\\n Then: Increase ticket prices by surge factor\\n\\nCondition:\\n When: passenger has not checked in 45 minutes before departure\\n Then: Release seat to standby list and notify passenger\\n\\nCondition:\\n When: loyalty member reaches tier threshold\\n Then: Automatically upgrade tier and send congratulations notification\\n\\nEvent:\\n On: FlightBooked\\n Do: Send confirmation email and add to passenger itinerary\\n\\nEvent:\\n On: CheckInCompleted\\n Do: Update flight manifest and assign boarding zone\\n\\nEvent:\\n On: FlightCancelled\\n Do: Notify all passengers, process refunds, and offer rebooking\\n\\nEvent:\\n On: BoardingStarted\\n Do: Call zones sequentially and track boarding progress\\n\\nParallel:\\n Booking\\n Loyalty\\n\\nOptimize:\\n Flight search speed\\n Priority: Critical\\n\\nOptimize:\\n Seat allocation revenue\\n Priority: High\\n\\nLearn:\\n demand_forecast\\n Based: route_history, seasonality, events, competition_pricing\\n Adapt: adjust pricing and capacity allocation based on predicted demand\\n\\nEncrypt:\\n passenger passport_number\\n passenger date_of_birth\\n\\nProtect:\\n Passenger identity from unauthorized access\\n Booking data from manipulation\\n Payment from fraud\"}

{\"id\": \"aicl_009\", \"domain\": \"music_streaming\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a music streaming service with artists, albums, playlists, tracks, subscriptions, and recommendation engine\", \"code\": \"# Music Streaming Service\\n# Levels 1-8: Audio platform with ML recommendations\\n\\nGoal:\\nCreate a music streaming service with artist management, album catalog, playlist creation, track playback, subscription billing, and AI-powered recommendations\\n\\nConstraint:\\nTrack playback must start within 2 seconds\\n\\nConstraint:\\nRecommendation engine must update within 1 hour of listening activity\\n\\nRisk:\\nMusic licensing expires mid-stream\\n\\nRecovery:\\nRemove track from catalog, notify users in active playlists, and substitute similar track\\n\\nRisk:\\nSubscription payment fails\\n\\nRecovery:\\nRetry payment and provide 7-day grace period before downgrading\\n\\nRisk:\\nRecommendation algorithm creates filter bubble\\n\\nRecovery:\\nInject 15 percent discovery tracks outside user comfort zone\\n\\nLayer:\\n Catalog\\n SubLayer:\\n Artists\\n SubLayer:\\n Albums\\n SubLayer:\\n Tracks\\n\\nLayer:\\n Playback\\n SubLayer:\\n Streaming\\n SubLayer:\\n Queue\\n\\nLayer:\\n Users\\n SubLayer:\\n Profiles\\n SubLayer:\\n Playlists\\n\\nLayer:\\n Subscriptions\\n SubLayer:\\n Plans\\n SubLayer:\\n Billing\\n\\nValidation:\\nTracks stream without interruption\\n\\nValidation:\\nPlaylists can be created, modified, and shared\\n\\nValidation:\\nRecommendations reflect listening history\\n\\nValidation:\\nSubscription billing is accurate\\n\\nEntity:\\n Artist\\n id: integer\\n name: string\\n genre: string\\n bio: string\\n monthly_listeners: integer\\n verified: boolean\\n\\nEntity:\\n Album\\n id: integer\\n artist_id: integer\\n title: string\\n release_date: datetime\\n genre: string\\n track_count: integer\\n cover_url: string\\n\\nEntity:\\n Track\\n id: integer\\n album_id: integer\\n title: string\\n duration: integer\\n play_count: integer\\n file_url: string\\n explicit: boolean\\n\\nEntity:\\n Playlist\\n id: integer\\n user_id: integer\\n name: string\\n tracks: list\\n is_public: boolean\\n created_at: datetime\\n\\nEntity:\\n User\\n id: integer\\n username: string\\n email: string\\n subscription_tier: string\\n listening_history: list\\n created_at: datetime\\n\\nEntity:\\n Subscription\\n id: integer\\n user_id: integer\\n plan: string\\n price: float\\n billing_date: datetime\\n status: string\\n\\nBehavior:\\n StreamTrack\\n Input: track_id integer, user_id integer, quality string\\n Output: stream_url string\\n Action: Authenticate user and return streaming URL with quality selection\\n\\nBehavior:\\n CreatePlaylist\\n Input: user_id integer, name string, tracks list, is_public boolean\\n Output: playlist Playlist\\n Action: Create playlist with initial tracks and visibility setting\\n\\nBehavior:\\n AddToPlaylist\\n Input: playlist_id integer, track_id integer, position integer\\n Output: updated_playlist Playlist\\n Action: Insert track at position and reorder if needed\\n\\nBehavior:\\n GenerateRecommendations\\n Input: user_id integer, count integer\\n Output: tracks list\\n Action: Analyze listening history and return personalized track suggestions\\n\\nBehavior:\\n ProcessSubscription\\n Input: user_id integer, plan string, payment_method string\\n Output: subscription Subscription\\n Action: Create or upgrade subscription and process initial payment\\n\\nBehavior:\\n RecordPlay\\n Input: user_id integer, track_id integer, duration_played integer\\n Output: play_record dict\\n Action: Log play event for analytics and recommendation training\\n\\nCondition:\\n When: user is on free tier and skips more than 6 tracks per hour\\n Then: Display audio ad before next track plays\\n\\nCondition:\\n When: track is marked explicit and user profile is under 18\\n Then: Block playback and suggest clean version\\n\\nCondition:\\n When: subscription payment fails\\n Then: Enter grace period and restrict premium features after 7 days\\n\\nEvent:\\n On: TrackPlayed\\n Do: Update play count, refresh recommendations, and record analytics\\n\\nEvent:\\n On: PlaylistCreated\\n Do: Index for search and notify followers if public\\n\\nEvent:\\n On: SubscriptionRenewed\\n Do: Extend access and send confirmation\\n\\nEvent:\\n On: ArtistVerified\\n Do: Update profile badge and boost in search results\\n\\nParallel:\\n Playback\\n Catalog\\n\\nOptimize:\\n Track streaming latency\\n Priority: Critical\\n\\nOptimize:\\n Recommendation relevance\\n Priority: High\\n\\nLearn:\\n track_recommendation\\n Based: listening_history, liked_tracks, playlist_additions, genre_affinity\\n Adapt: weight recommendation factors based on skip rate and save rate\\n\\nLearn:\\n mood_detection\\n Based: listening_patterns, time_of_day, track_features\\n Adapt: suggest playlists matching detected mood context\"}

{\"id\": \"aicl_010\", \"domain\": \"project_management\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a project management tool with tasks, teams, sprints, deadlines, and workload balancing\", \"code\": \"# Project Management Tool\\n# Levels 1-8: Agile project tracking with adaptive workload\\n\\nGoal:\\nCreate a project management tool with task boards, team management, sprint planning, deadline tracking, and adaptive workload balancing\\n\\nConstraint:\\nTask board must update in real-time across all team members\\n\\nConstraint:\\nSprint velocity calculations must be accurate\\n\\nRisk:\\nSprint scope exceeds team capacity\\n\\nRecovery:\\nAlert scrum master and suggest scope reduction or timeline extension\\n\\nRisk:\\nCritical path task blocked by dependency\\n\\nRecovery:\\nEscalate to project lead and reassign or parallelize dependency\\n\\nRisk:\\nTeam member overloaded with tasks\\n\\nRecovery:\\nRedistribute tasks to available team members with matching skills\\n\\nLayer:\\n Projects\\n SubLayer:\\n Boards\\n SubLayer:\\n Timelines\\n\\nLayer:\\n Tasks\\n SubLayer:\\n Items\\n SubLayer:\\n Dependencies\\n\\nLayer:\\n Teams\\n SubLayer:\\n Members\\n SubLayer:\\n Workload\\n\\nLayer:\\n Sprints\\n SubLayer:\\n Planning\\n SubLayer:\\n Velocity\\n\\nValidation:\\nTasks can be created, assigned, and moved through statuses\\n\\nValidation:\\nSprint planning respects team capacity\\n\\nValidation:\\nDependencies are tracked and blockers identified\\n\\nEntity:\\n Project\\n id: integer\\n name: string\\n description: string\\n owner_id: integer\\n status: string\\n start_date: datetime\\n deadline: datetime\\n\\nEntity:\\n Task\\n id: integer\\n project_id: integer\\n title: string\\n description: string\\n assignee_id: integer\\n status: string\\n priority: string\\n story_points: integer\\n due_date: datetime\\n labels: list\\n\\nEntity:\\n TeamMember\\n id: integer\\n name: string\\n role: string\\n skills: list\\n availability: float\\n current_load: float\\n\\nEntity:\\n Sprint\\n id: integer\\n project_id: integer\\n name: string\\n start_date: datetime\\n end_date: datetime\\n velocity: integer\\n capacity: integer\\n\\nEntity:\\n Dependency\\n blocker_task_id: integer\\n blocked_task_id: integer\\n type: string\\n\\nBehavior:\\n CreateTask\\n Input: project_id integer, title string, description string, priority string\\n Output: task Task\\n Action: Create task in backlog with default status and no assignee\\n\\nBehavior:\\n AssignTask\\n Input: task_id integer, member_id integer\\n Output: updated_task Task\\n Action: Assign task to team member and update workload\\n\\nBehavior:\\n MoveTask\\n Input: task_id integer, new_status string\\n Output: updated_task Task\\n Action: Move task to new status column and log transition\\n\\nBehavior:\\n PlanSprint\\n Input: project_id integer, tasks list, team_capacity integer\\n Output: sprint Sprint\\n Action: Validate capacity and create sprint with selected tasks\\n\\nBehavior:\\n CalculateVelocity\\n Input: sprint_id integer\\n Output: velocity integer\\n Action: Sum completed story points from finished sprint\\n\\nBehavior:\\n BalanceWorkload\\n Input: team_members list, tasks list\\n Output: assignments dict\\n Action: Redistribute tasks based on availability and skill matching\\n\\nCondition:\\n When: task due date is within 24 hours and status is not complete\\n Then: Escalate to project lead and flag as at-risk\\n\\nCondition:\\n When: sprint completion rate falls below 60 percent at midpoint\\n Then: Alert scrum master and suggest scope adjustment\\n\\nCondition:\\n When: team member workload exceeds 120 percent capacity\\n Then: Flag overload and suggest task redistribution\\n\\nEvent:\\n On: TaskCompleted\\n Do: Update sprint burndown, notify dependent tasks, and check sprint goals\\n\\nEvent:\\n On: SprintStarted\\n Do: Lock sprint scope and begin velocity tracking\\n\\nEvent:\\n On: DependencyResolved\\n Do: Unblock dependent tasks and notify assignees\\n\\nEvent:\\n On: DeadlineApproaching\\n Do: Send reminders to assignees and alert project lead\\n\\nParallel:\\n Tasks\\n Teams\\n\\nOptimize:\\n Task board refresh speed\\n Priority: High\\n\\nOptimize:\\n Sprint planning efficiency\\n Priority: Medium\\n\\nLearn:\\n velocity_prediction\\n Based: historical_velocity, team_composition, task_complexity, sprint_length\\n Adapt: adjust sprint capacity estimates based on actual vs predicted velocity\\n\\nLearn:\\n task_duration_estimate\\n Based: task_type, assignee_history, complexity_indicators, dependencies_count\\n Adapt: refine story point estimates based on actual completion times\"}

{\"id\": \"aicl_011\", \"domain\": \"restaurant_delivery\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a restaurant delivery platform with restaurant management, orders, drivers, menus, and real-time tracking\", \"code\": \"# Restaurant Delivery Platform\\n# Levels 1-7: Food delivery with real-time tracking\\n\\nGoal:\\nCreate a restaurant delivery platform with restaurant management, menu browsing, order placement, driver dispatch, and real-time delivery tracking\\n\\nConstraint:\\nOrder to delivery must complete within 45 minutes\\n\\nConstraint:\\nDriver matching must complete within 30 seconds\\n\\nRisk:\\nRestaurant unable to fulfill order items\\n\\nRecovery:\\nNotify customer of unavailable items and offer substitutes or refund\\n\\nRisk:\\nDelivery driver stuck in traffic\\n\\nRecovery:\\nRecalculate ETA and notify customer with updated estimate\\n\\nRisk:\\nFood arrives cold or damaged\\n\\nRecovery:\\nIssue refund or redelivery and flag restaurant for quality review\\n\\nLayer:\\n Restaurants\\n SubLayer:\\n Menu\\n SubLayer:\\n Kitchen\\n\\nLayer:\\n Orders\\n SubLayer:\\n Placement\\n SubLayer:\\n Tracking\\n\\nLayer:\\n Delivery\\n SubLayer:\\n Dispatch\\n SubLayer:\\n Routing\\n\\nValidation:\\nCustomers can browse menus and place orders\\n\\nValidation:\\nOrders are dispatched to nearest available driver\\n\\nValidation:\\nDelivery progress is tracked in real-time\\n\\nEntity:\\n Restaurant\\n id: integer\\n name: string\\n cuisine: string\\n address: string\\n rating: float\\n delivery_radius: integer\\n is_open: boolean\\n prep_time_minutes: integer\\n\\nEntity:\\n MenuItem\\n id: integer\\n restaurant_id: integer\\n name: string\\n description: string\\n price: float\\n category: string\\n available: boolean\\n allergens: list\\n\\nEntity:\\n Order\\n id: integer\\n customer_id: integer\\n restaurant_id: integer\\n items: list\\n total: float\\n status: string\\n placed_at: datetime\\n estimated_delivery: datetime\\n\\nEntity:\\n Driver\\n id: integer\\n name: string\\n phone: string\\n vehicle_type: string\\n is_available: boolean\\n current_location: dict\\n rating: float\\n\\nEntity:\\n Delivery\\n id: integer\\n order_id: integer\\n driver_id: integer\\n pickup_time: datetime\\n delivery_time: datetime\\n status: string\\n route: list\\n\\nBehavior:\\n BrowseMenu\\n Input: restaurant_id integer, category string\\n Output: items list\\n Action: Return available menu items filtered by category\\n\\nBehavior:\\n PlaceOrder\\n Input: customer_id integer, restaurant_id integer, items list, delivery_address dict\\n Output: order Order\\n Action: Validate items, calculate total, and create order\\n\\nBehavior:\\n DispatchDriver\\n Input: order_id integer, restaurant_location dict\\n Output: delivery Delivery\\n Action: Find nearest available driver and assign delivery\\n\\nBehavior:\\n UpdateDeliveryStatus\\n Input: delivery_id integer, status string, location dict\\n Output: updated_delivery Delivery\\n Action: Update delivery progress and notify customer\\n\\nBehavior:\\n CalculateDeliveryFee\\n Input: distance float, order_total float, demand_level float\\n Output: fee float\\n Action: Compute delivery fee based on distance and demand\\n\\nCondition:\\n When: order preparation exceeds estimated prep time by 10 minutes\\n Then: Notify customer of delay and update delivery estimate\\n\\nCondition:\\n When: no drivers available within delivery radius\\n Then: Queue order and notify customer of extended wait time\\n\\nEvent:\\n On: OrderPlaced\\n Do: Send order to restaurant kitchen and start driver dispatch\\n\\nEvent:\\n On: OrderReady\\n Do: Notify assigned driver for pickup\\n\\nEvent:\\n On: DeliveryCompleted\\n Do: Process payment, request ratings, and update driver availability\\n\\nParallel:\\n Orders\\n Delivery\\n\\nOptimize:\\n Driver matching speed\\n Priority: Critical\\n\\nOptimize:\\n Delivery route efficiency\\n Priority: High\"}

{\"id\": \"aicl_012\", \"domain\": \"cryptocurrency_exchange\", \"levels\": [1,2,3,4,5,6,7,8,9,10], \"description\": \"Build a cryptocurrency exchange with wallets, trading, order book, ledger, and compliance\", \"code\": \"# Cryptocurrency Exchange\\n# All 10 Levels: Digital asset trading platform\\n\\nGoal:\\nCreate a cryptocurrency exchange with wallet management, order book trading, real-time market data, transaction ledger, and regulatory compliance\\n\\nConstraint:\\nOrder matching must complete within 100 milliseconds\\n\\nConstraint:\\nMust comply with KYC and AML regulations\\n\\nConstraint:\\nAll transactions must be cryptographically verifiable\\n\\nRisk:\\nFlash crash or extreme market volatility\\n\\nRecovery:\\nActivate circuit breaker and halt trading for cooling period\\n\\nRisk:\\nWallet security breach\\n\\nRecovery:\\nFreeze affected wallets, initiate security audit, and notify authorities\\n\\nRisk:\\nDouble spend attack\\n\\nRecovery:\\nReject transaction, flag account, and increase confirmation requirements\\n\\nRisk:\\nExchange liquidity insufficient for large order\\n\\nRecovery:\\nSplit order across time and notify trader of execution risk\\n\\nLayer:\\n Trading\\n SubLayer:\\n OrderBook\\n SubLayer:\\n Matching\\n\\nLayer:\\n Wallets\\n SubLayer:\\n Balances\\n SubLayer:\\n Transactions\\n\\nLayer:\\n Market\\n SubLayer:\\n Prices\\n SubLayer:\\n Analytics\\n\\nLayer:\\n Compliance\\n SubLayer:\\n KYC\\n SubLayer:\\n AML\\n\\nValidation:\\nOrders are matched fairly by price and time priority\\n\\nValidation:\\nWallet balances are always consistent with transaction history\\n\\nValidation:\\nKYC verification is completed before trading is enabled\\n\\nValidation:\\nAll transactions are recorded in immutable ledger\\n\\nEntity:\\n Wallet\\n id: integer\\n user_id: integer\\n currency: string\\n balance: float\\n locked_balance: float\\n address: string\\n\\nEntity:\\n Order\\n id: integer\\n user_id: integer\\n pair: string\\n side: string\\n type: string\\n price: float\\n amount: float\\n filled: float\\n status: string\\n created_at: datetime\\n\\nEntity:\\n Trade\\n id: integer\\n buy_order_id: integer\\n sell_order_id: integer\\n pair: string\\n price: float\\n amount: float\\n executed_at: datetime\\n\\nEntity:\\n Transaction\\n id: integer\\n wallet_id: integer\\n type: string\\n amount: float\\n hash: string\\n status: string\\n created_at: datetime\\n\\nEntity:\\n User\\n id: integer\\n email: string\\n kyc_status: string\\n trading_enabled: boolean\\n risk_level: string\\n\\nBehavior:\\n PlaceOrder\\n Input: user_id integer, pair string, side string, type string, price float, amount float\\n Output: order Order\\n Action: Validate balance, create order, and submit to order book\\n\\nBehavior:\\n MatchOrders\\n Input: pair string, incoming_order Order\\n Output: trades list\\n Action: Match incoming order against book by price-time priority\\n\\nBehavior:\\n ExecuteTrade\\n Input: buy_order Order, sell_order Order, match_price float, match_amount float\\n Output: trade Trade\\n Action: Execute matched trade, update balances, and record transaction\\n\\nBehavior:\\n DepositFunds\\n Input: user_id integer, currency string, amount float, tx_hash string\\n Output: transaction Transaction\\n Action: Credit wallet after blockchain confirmation\\n\\nBehavior:\\n WithdrawFunds\\n Input: user_id integer, currency string, amount float, destination string\\n Output: transaction Transaction\\n Action: Debit wallet and submit withdrawal to blockchain\\n\\nBehavior:\\n VerifyKYC\\n Input: user_id integer, documents list\\n Output: verification dict\\n Action: Verify identity documents and enable trading if approved\\n\\nCondition:\\n When: price moves more than 20 percent in 5 minutes\\n Then: Activate circuit breaker and halt trading for 15 minutes\\n\\nCondition:\\n When: user KYC is not verified and order amount exceeds daily limit\\n Then: Block order and prompt user to complete KYC verification\\n\\nCondition:\\n When: wallet balance is insufficient for order\\n Then: Reject order and notify user of insufficient funds\\n\\nEvent:\\n On: OrderPlaced\\n Do: Add to order book and attempt matching\\n\\nEvent:\\n On: TradeExecuted\\n Do: Update both wallets, record in ledger, and publish market data\\n\\nEvent:\\n On: DepositConfirmed\\n Do: Credit wallet balance and notify user\\n\\nEvent:\\n On: CircuitBreakerTriggered\\n Do: Halt all trading and notify all users\\n\\nParallel:\\n Trading\\n Market\\n\\nOptimize:\\n Order matching latency\\n Priority: Critical\\n\\nOptimize:\\n Market data refresh rate\\n Priority: High\\n\\nLearn:\\n fraud_detection\\n Based: trading_patterns, withdrawal_frequency, ip_geolocation, device_fingerprint\\n Adapt: increase verification requirements when fraud_probability exceeds 70 percent\\n\\nLearn:\\n price_prediction\\n Based: order_book_imbalance, volume_trends, market_sentiment, correlated_assets\\n Adapt: adjust risk limits based on predicted volatility\\n\\nEncrypt:\\n wallet private_keys\\n user identity_documents\\n\\nProtect:\\n Wallet balances from unauthorized modification\\n Order book from manipulation\\n Transaction history from tampering\\n\\nNative:\\n blockchain_monitor\\n Language: Python\\n Code: \\\"import asyncio; from web3 import Web3; w3 = Web3(Web3.WebsocketProvider(WS_URL)); pending = w3.eth.filter('pending')\\\"\"}

{\"id\": \"aicl_013\", \"domain\": \"weather_monitoring\", \"levels\": [1,2,3,4,5], \"description\": \"Build a weather monitoring system with stations, readings, alerts, and forecasts\", \"code\": \"# Weather Monitoring System\\n# Levels 1-5: Simple meteorological tracking\\n\\nGoal:\\nCreate a weather monitoring system with sensor stations, real-time readings, threshold alerts, and weather forecasts\\n\\nConstraint:\\nReadings must be collected every 5 minutes\\n\\nRisk:\\nSensor station goes offline\\n\\nRecovery:\\nUse last known reading and flag station for maintenance\\n\\nRisk:\\nExtreme weather alert missed\\n\\nRecovery:\\nTrigger redundant alert system and notify emergency services\\n\\nLayer:\\n Stations\\n SubLayer:\\n Sensors\\n\\nLayer:\\n Readings\\n SubLayer:\\n Collection\\n\\nLayer:\\n Alerts\\n SubLayer:\\n Thresholds\\n\\nValidation:\\nReadings are collected at regular intervals\\n\\nValidation:\\nAlerts fire when thresholds are exceeded\\n\\nEntity:\\n Station\\n id: integer\\n name: string\\n location: string\\n latitude: float\\n longitude: float\\n altitude: float\\n is_active: boolean\\n\\nEntity:\\n Reading\\n id: integer\\n station_id: integer\\n temperature: float\\n humidity: float\\n pressure: float\\n wind_speed: float\\n wind_direction: string\\n timestamp: datetime\\n\\nEntity:\\n Alert\\n id: integer\\n station_id: integer\\n type: string\\n severity: string\\n message: string\\n is_active: boolean\\n created_at: datetime\\n\\nBehavior:\\n CollectReading\\n Input: station_id integer, temperature float, humidity float, pressure float, wind_speed float\\n Output: reading Reading\\n Action: Store weather reading and check alert thresholds\\n\\nBehavior:\\n CheckThresholds\\n Input: reading Reading\\n Output: alerts list\\n Action: Compare reading values against configured thresholds\\n\\nBehavior:\\n GenerateForecast\\n Input: station_id integer, hours_ahead integer\\n Output: forecast dict\\n Action: Predict weather conditions based on recent trends\\n\\nBehavior:\\n NotifyAlert\\n Input: alert Alert\\n Output: notification dict\\n Action: Send alert to subscribers through configured channels\\n\\nCondition:\\n When: temperature exceeds 40 degrees celsius\\n Then: Issue heat wave warning and activate public safety protocol\\n\\nCondition:\\n When: wind speed exceeds 90 km/h\\n Then: Issue storm warning and recommend shelter\\n\\nEvent:\\n On: ReadingCollected\\n Do: Update dashboard and check alert thresholds\\n\\nEvent:\\n On: AlertTriggered\\n Do: Send notifications and log in alert history\"}

{\"id\": \"aicl_014\", \"domain\": \"library_management\", \"levels\": [1,2,3,4,5,6], \"description\": \"Build a library management system with books, members, loans, reservations, and overdue tracking\", \"code\": \"# Library Management System\\n# Levels 1-6: Book lending with concurrency\\n\\nGoal:\\nCreate a library management system with book catalog, member registration, loan processing, reservation queue, overdue tracking, and fine calculation\\n\\nConstraint:\\nEach member may borrow up to 10 books simultaneously\\n\\nConstraint:\\nOverdue fines must be calculated daily\\n\\nRisk:\\nBook lost or damaged by member\\n\\nRecovery:\\nCharge replacement fee and update inventory\\n\\nRisk:\\nPopular book has long reservation queue\\n\\nRecovery:\\nReduce loan period for high-demand books and purchase additional copies\\n\\nRisk:\\nMember accumulates excessive fines\\n\\nRecovery:\\nSuspend borrowing privileges until fines are paid or arranged\\n\\nLayer:\\n Catalog\\n SubLayer:\\n Books\\n SubLayer:\\n Search\\n\\nLayer:\\n Members\\n SubLayer:\\n Registration\\n SubLayer:\\n Accounts\\n\\nLayer:\\n Circulation\\n SubLayer:\\n Loans\\n SubLayer:\\n Returns\\n\\nLayer:\\n Reservations\\n SubLayer:\\n Queue\\n SubLayer:\\n Notifications\\n\\nValidation:\\nBooks can be checked out and returned correctly\\n\\nValidation:\\nOverdue fines are calculated accurately\\n\\nValidation:\\nReservation queue operates fairly\\n\\nEntity:\\n Book\\n id: integer\\n title: string\\n author: string\\n isbn: string\\n category: string\\n publication_year: integer\\n status: string\\n location: string\\n\\nEntity:\\n Member\\n id: integer\\n name: string\\n email: string\\n phone: string\\n membership_type: string\\n books_borrowed: integer\\n outstanding_fines: float\\n status: string\\n\\nEntity:\\n Loan\\n id: integer\\n book_id: integer\\n member_id: integer\\n checkout_date: datetime\\n due_date: datetime\\n return_date: datetime\\n status: string\\n\\nEntity:\\n Reservation\\n id: integer\\n book_id: integer\\n member_id: integer\\n position: integer\\n created_at: datetime\\n status: string\\n\\nEntity:\\n Fine\\n id: integer\\n member_id: integer\\n loan_id: integer\\n amount: float\\n days_overdue: integer\\n status: string\\n\\nBehavior:\\n CheckoutBook\\n Input: book_id integer, member_id integer\\n Output: loan Loan\\n Action: Verify availability and member eligibility, create loan record\\n\\nBehavior:\\n ReturnBook\\n Input: loan_id integer\\n Output: returned_loan Loan\\n Action: Process return, calculate fines if overdue, check reservation queue\\n\\nBehavior:\\n ReserveBook\\n Input: book_id integer, member_id integer\\n Output: reservation Reservation\\n Action: Add member to reservation queue if book is unavailable\\n\\nBehavior:\\n CalculateFine\\n Input: loan_id integer\\n Output: fine Fine\\n Action: Compute daily overdue fine from due date to return date\\n\\nBehavior:\\n SearchCatalog\\n Input: query string, category string\\n Output: books list\\n Action: Search books by title, author, or ISBN with filters\\n\\nBehavior:\\n RenewLoan\\n Input: loan_id integer\\n Output: renewed_loan Loan\\n Action: Extend due date if no reservations exist for the book\\n\\nCondition:\\n When: book is overdue by more than 30 days\\n Then: Mark as lost and charge replacement fee to member\\n\\nCondition:\\n When: member outstanding_fines exceed 25 dollars\\n Then: Suspend borrowing privileges until payment is made\\n\\nCondition:\\n When: reservation queue for a book exceeds 5 members\\n Then: Reduce loan period to 7 days and order additional copy\\n\\nEvent:\\n On: BookCheckedOut\\n Do: Update book status, decrement member borrow count, set due date\\n\\nEvent:\\n On: BookReturned\\n Do: Update book status, increment member borrow count, notify next reservation\\n\\nEvent:\\n On: ReservationAvailable\\n Do: Notify member that reserved book is ready for pickup\\n\\nParallel:\\n Catalog\\n Circulation\"}

{\"id\": \"aicl_015\", \"domain\": \"fitness_tracker\", \"levels\": [1,2,3,4,5,7,8], \"description\": \"Build a fitness tracker with workouts, goals, progress tracking, challenges, and adaptive coaching\", \"code\": \"# Fitness Tracker Application\\n# Levels 1-5, 7-8: Health and workout platform with ML coaching\\n\\nGoal:\\nCreate a fitness tracker with workout logging, goal setting, progress tracking, social challenges, and AI-powered adaptive coaching\\n\\nConstraint:\\nWorkout data must sync across devices within 5 seconds\\n\\nConstraint:\\nHeart rate alerts must trigger within 3 seconds\\n\\nRisk:\\nUser overexertion detected via heart rate\\n\\nRecovery:\\nAlert user to stop exercise and display recovery breathing guide\\n\\nRisk:\\nGoal progress stagnation for over 2 weeks\\n\\nRecovery:\\nSuggest program variation and reduce intensity targets\\n\\nRisk:\\nSync conflict between devices\\n\\nRecovery:\\nMerge data with server-side conflict resolution preferring most recent\\n\\nLayer:\\n Workouts\\n SubLayer:\\n Logging\\n SubLayer:\\n Analysis\\n\\nLayer:\\n Goals\\n SubLayer:\\n Setting\\n SubLayer:\\n Progress\\n\\nLayer:\\n Social\\n SubLayer:\\n Challenges\\n SubLayer:\\n Leaderboards\\n\\nValidation:\\nWorkouts are logged accurately with duration and metrics\\n\\nValidation:\\nGoal progress is calculated correctly\\n\\nValidation:\\nChallenges track participant progress fairly\\n\\nEntity:\\n User\\n id: integer\\n name: string\\n age: integer\\n weight: float\\n height: float\\n fitness_level: string\\n resting_heart_rate: integer\\n\\nEntity:\\n Workout\\n id: integer\\n user_id: integer\\n type: string\\n duration: integer\\n calories: integer\\n heart_rate_avg: integer\\n heart_rate_max: integer\\n distance: float\\n notes: string\\n completed_at: datetime\\n\\nEntity:\\n Goal\\n id: integer\\n user_id: integer\\n metric: string\\n target_value: float\\n current_value: float\\n deadline: datetime\\n status: string\\n\\nEntity:\\n Challenge\\n id: integer\\n name: string\\n metric: string\\n target_value: float\\n start_date: datetime\\n end_date: datetime\\n participants: list\\n\\nEntity:\\n Achievement\\n id: integer\\n user_id: integer\\n type: string\\n name: string\\n earned_at: datetime\\n\\nBehavior:\\n LogWorkout\\n Input: user_id integer, type string, duration integer, metrics dict\\n Output: workout Workout\\n Action: Record workout data and update goal progress\\n\\nBehavior:\\n SetGoal\\n Input: user_id integer, metric string, target float, deadline datetime\\n Output: goal Goal\\n Action: Create goal with deadline and initial progress\\n\\nBehavior:\\n UpdateProgress\\n Input: goal_id integer, new_value float\\n Output: updated_goal Goal\\n Action: Update goal progress and check if achieved\\n\\nBehavior:\\n JoinChallenge\\n Input: user_id integer, challenge_id integer\\n Output: participation dict\\n Action: Register user for challenge and initialize progress\\n\\nBehavior:\\n GenerateCoachingPlan\\n Input: user_id integer, goals list, fitness_level string\\n Output: plan dict\\n Action: Create personalized workout plan based on goals and history\\n\\nBehavior:\\n CalculateCalories\\n Input: user User, workout_type string, duration integer, intensity string\\n Output: calories integer\\n Action: Estimate calorie burn using user metrics and activity data\\n\\nCondition:\\n When: heart rate exceeds 90 percent of age-predicted maximum\\n Then: Alert user to reduce intensity and display recovery zone\\n\\nCondition:\\n When: goal deadline is within 7 days and progress below 70 percent\\n Then: Suggest revised target or intensified workout schedule\\n\\nCondition:\\n When: workout streak reaches 30 consecutive days\\n Then: Award achievement badge and share milestone\\n\\nEvent:\\n On: WorkoutCompleted\\n Do: Update goals, check achievements, and refresh coaching plan\\n\\nEvent:\\n On: GoalAchieved\\n Do: Award achievement, notify followers, and suggest next goal\\n\\nEvent:\\n On: ChallengeCompleted\\n Do: Rank participants and distribute rewards\\n\\nOptimize:\\n Data sync speed\\n Priority: Critical\\n\\nOptimize:\\n Coaching plan relevance\\n Priority: High\\n\\nLearn:\\n performance_prediction\\n Based: workout_history, recovery_time, sleep_data, nutrition_log\\n Adapt: adjust workout intensity and recovery periods based on predicted readiness\\n\\nLearn:\\n plate_detection\\n Based: progress_trend, workout_variety, heart_rate_response, subjective_fatigue\\n Adapt: recommend program deload or variation when plateau probability exceeds 60 percent\"}

{\"id\": \"aicl_016\", \"domain\": \"real_estate\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a real estate platform with property listings, agents, viewings, offers, and mortgage calculation\", \"code\": \"# Real Estate Platform\\n# Levels 1-7: Property marketplace with scheduling\\n\\nGoal:\\nCreate a real estate platform with property listings, agent management, viewing scheduling, offer management, and mortgage calculation\\n\\nConstraint:\\nProperty search must return results within 1 second\\n\\nConstraint:\\nViewing confirmations must be sent within 30 seconds\\n\\nRisk:\\nProperty listing contains inaccurate information\\n\\nRecovery:\\nFlag listing for review and notify agent to verify details\\n\\nRisk:\\nViewing schedule conflict\\n\\nRecovery:\\nDetect overlap and propose alternative time slots\\n\\nRisk:\\nOffer below minimum acceptable price\\n\\nRecovery:\\nAuto-reject and inform buyer of minimum threshold\\n\\nLayer:\\n Properties\\n SubLayer:\\n Listings\\n SubLayer:\\n Search\\n\\nLayer:\\n Agents\\n SubLayer:\\n Profiles\\n SubLayer:\\n Schedule\\n\\nLayer:\\n Transactions\\n SubLayer:\\n Viewings\\n SubLayer:\\n Offers\\n\\nValidation:\\nProperties can be listed with accurate details and photos\\n\\nValidation:\\nViewings can be scheduled without conflicts\\n\\nValidation:\\nOffers are tracked and negotiated correctly\\n\\nEntity:\\n Property\\n id: integer\\n address: string\\n price: float\\n bedrooms: integer\\n bathrooms: integer\\n square_feet: integer\\n property_type: string\\n status: string\\n agent_id: integer\\n features: list\\n photos: list\\n\\nEntity:\\n Agent\\n id: integer\\n name: string\\n email: string\\n phone: string\\n license_number: string\\n specialization: string\\n rating: float\\n listings_count: integer\\n\\nEntity:\\n Viewing\\n id: integer\\n property_id: integer\\n buyer_id: integer\\n agent_id: integer\\n scheduled_at: datetime\\n duration: integer\\n status: string\\n notes: string\\n\\nEntity:\\n Offer\\n id: integer\\n property_id: integer\\n buyer_id: integer\\n amount: float\\n conditions: list\\n status: string\\n submitted_at: datetime\\n response_deadline: datetime\\n\\nEntity:\\n Buyer\\n id: integer\\n name: string\\n email: string\\n phone: string\\n budget_max: float\\n preferred_areas: list\\n pre_approved: boolean\\n\\nBehavior:\\n ListProperty\\n Input: address string, price float, details dict, agent_id integer\\n Output: property Property\\n Action: Create property listing with details and assign to agent\\n\\nBehavior:\\n ScheduleViewing\\n Input: property_id integer, buyer_id integer, agent_id integer, preferred_time datetime\\n Output: viewing Viewing\\n Action: Check availability and schedule property viewing\\n\\nBehavior:\\n SubmitOffer\\n Input: property_id integer, buyer_id integer, amount float, conditions list\\n Output: offer Offer\\n Action: Submit purchase offer and notify listing agent\\n\\nBehavior:\\n CalculateMortgage\\n Input: price float, down_payment float, rate float, term_years integer\\n Output: mortgage dict\\n Action: Calculate monthly payment, total interest, and amortization schedule\\n\\nBehavior:\\n SearchProperties\\n Input: criteria dict, page integer\\n Output: properties list\\n Action: Filter properties matching search criteria\\n\\nCondition:\\n When: offer amount is below 90 percent of listing price\\n Then: Flag as lowball offer and advise agent on counter-offer strategy\\n\\nCondition:\\n When: property has been listed for over 90 days\\n Then: Suggest price reduction to agent and seller\\n\\nEvent:\\n On: PropertyListed\\n Do: Index for search, notify matching buyers, and add to agent portfolio\\n\\nEvent:\\n On: OfferSubmitted\\n Do: Notify seller agent and start response timer\\n\\nEvent:\\n On: ViewingCompleted\\n Do: Collect feedback from buyer and update agent notes\\n\\nParallel:\\n Properties\\n Transactions\\n\\nOptimize:\\n Search performance\\n Priority: Critical\\n\\nOptimize:\\n Mortgage calculation accuracy\\n Priority: High\"}

{\"id\": \"aicl_017\", \"domain\": \"supply_chain\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a supply chain logistics system with shipments, routes, warehouses, customs, and predictive analytics\", \"code\": \"# Supply Chain Logistics\\n# Levels 1-8: Global shipping with predictive analytics\\n\\nGoal:\\nCreate a supply chain logistics system with shipment tracking, route optimization, warehouse management, customs processing, and predictive delay analytics\\n\\nConstraint:\\nShipment tracking must update every 15 minutes\\n\\nConstraint:\\nCustoms documentation must be generated automatically\\n\\nRisk:\\nShipment delayed at customs\\n\\nRecovery:\\nEngage customs broker and notify consignee of revised ETA\\n\\nRisk:\\nWarehouse capacity exceeded during peak season\\n\\nRecovery:\\nActivate overflow warehouse and reroute incoming shipments\\n\\nRisk:\\nRoute disruption due to weather or port closure\\n\\nRecovery:\\nRecalculate route and reroute through alternative port\\n\\nRisk:\\nSupplier fails to deliver on schedule\\n\\nRecovery:\\nActivate backup supplier and adjust production timeline\\n\\nLayer:\\n Shipments\\n SubLayer:\\n Tracking\\n SubLayer:\\n Documentation\\n\\nLayer:\\n Routes\\n SubLayer:\\n Planning\\n SubLayer:\\n Optimization\\n\\nLayer:\\n Warehouses\\n SubLayer:\\n Capacity\\n SubLayer:\\n Operations\\n\\nLayer:\\n Compliance\\n SubLayer:\\n Customs\\n SubLayer:\\n Regulations\\n\\nValidation:\\nShipments are tracked from origin to destination\\n\\nValidation:\\nRoutes are optimized for cost and time\\n\\nValidation:\\nCustoms documentation is complete and accurate\\n\\nEntity:\\n Shipment\\n id: integer\\n origin: string\\n destination: string\\n status: string\\n carrier: string\\n weight: float\\n value: float\\n estimated_arrival: datetime\\n actual_arrival: datetime\\n\\nEntity:\\n Route\\n id: integer\\n origin: string\\n destination: string\\n waypoints: list\\n distance: float\\n estimated_duration: integer\\n mode: string\\n\\nEntity:\\n Warehouse\\n id: integer\\n name: string\\n location: string\\n capacity: integer\\n current_usage: integer\\n temperature_controlled: boolean\\n\\nEntity:\\n CustomsEntry\\n id: integer\\n shipment_id: integer\\n country: string\\n status: string\\n documents: list\\n duty_amount: float\\n cleared_at: datetime\\n\\nEntity:\\n Supplier\\n id: integer\\n name: string\\n country: string\\n lead_time_days: integer\\n reliability_score: float\\n products: list\\n\\nBehavior:\\n CreateShipment\\n Input: origin string, destination string, items list, carrier string\\n Output: shipment Shipment\\n Action: Create shipment record with tracking number and documentation\\n\\nBehavior:\\n OptimizeRoute\\n Input: origin string, destination string, constraints dict\\n Output: route Route\\n Action: Calculate optimal route considering cost, time, and risk factors\\n\\nBehavior:\\n TrackShipment\\n Input: shipment_id integer\\n Output: status dict\\n Action: Retrieve current location and status of shipment\\n\\nBehavior:\\n ProcessCustoms\\n Input: shipment_id integer, country string, documents list\\n Output: entry CustomsEntry\\n Action: Submit customs documentation and track clearance status\\n\\nBehavior:\\n ManageCapacity\\n Input: warehouse_id integer, incoming integer, outgoing integer\\n Output: capacity dict\\n Action: Update warehouse capacity and trigger overflow if needed\\n\\nCondition:\\n When: shipment is delayed by more than 48 hours\\n Then: Notify consignee, activate backup route, and adjust downstream schedule\\n\\nCondition:\\n When: warehouse usage exceeds 90 percent capacity\\n Then: Activate overflow facility and reroute incoming shipments\\n\\nCondition:\\n When: customs clearance exceeds 5 business days\\n Then: Engage customs broker and escalate to trade compliance team\\n\\nEvent:\\n On: ShipmentDeparted\\n Do: Start tracking updates and notify consignee of departure\\n\\nEvent:\\n On: CustomsCleared\\n Do: Update shipment status and schedule final delivery\\n\\nEvent:\\n On: DeliveryCompleted\\n Do: Close shipment record and update supplier reliability score\\n\\nParallel:\\n Shipments\\n Routes\\n\\nOptimize:\\n Route calculation speed\\n Priority: High\\n\\nOptimize:\\n Warehouse utilization\\n Priority: High\\n\\nLearn:\\n delay_prediction\\n Based: route_history, carrier_performance, weather_data, port_congestion, season\\n Adapt: add buffer time to routes with high delay probability and pre-clear customs\"}

{\"id\": \"aicl_018\", \"domain\": \"event_ticketing\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build an event ticketing system with events, venues, tickets, seating, and pricing\", \"code\": \"# Event Ticketing System\\n# Levels 1-7: Ticket sales with dynamic pricing\\n\\nGoal:\\nCreate an event ticketing system with event management, venue seating, ticket sales, dynamic pricing, and entry management\\n\\nConstraint:\\nTicket purchase must complete within 10 seconds\\n\\nConstraint:\\nSeating availability must be real-time accurate\\n\\nRisk:\\nTicket scalping or bot purchases\\n\\nRecovery:\\nImplement purchase limits, CAPTCHA, and queue system for high-demand events\\n\\nRisk:\\nEvent cancellation\\n\\nRecovery:\\nProcess full refunds automatically and notify all ticket holders\\n\\nRisk:\\nOverselling seats due to concurrent purchases\\n\\nRecovery:\\nImplement row-level locking and hold seats during checkout\\n\\nLayer:\\n Events\\n SubLayer:\\n Management\\n SubLayer:\\n Schedule\\n\\nLayer:\\n Venues\\n SubLayer:\\n Seating\\n SubLayer:\\n Layout\\n\\nLayer:\\n Tickets\\n SubLayer:\\n Sales\\n SubLayer:\\n Pricing\\n\\nLayer:\\n Entry\\n SubLayer:\\n Validation\\n SubLayer:\\n Access\\n\\nValidation:\\nTickets can be purchased and transferred correctly\\n\\nValidation:\\nSeating availability is accurate in real-time\\n\\nValidation:\\nDynamic pricing reflects demand correctly\\n\\nEntity:\\n Event\\n id: integer\\n name: string\\n venue_id: integer\\n date: datetime\\n category: string\\n status: string\\n base_price: float\\n total_capacity: integer\\n tickets_sold: integer\\n\\nEntity:\\n Venue\\n id: integer\\n name: string\\n address: string\\n capacity: integer\\n sections: list\\n layout: dict\\n\\nEntity:\\n Ticket\\n id: integer\\n event_id: integer\\n section: string\\n row: string\\n seat: string\\n price: float\\n status: string\\n holder_name: string\\n purchased_at: datetime\\n\\nEntity:\\n PricingTier\\n id: integer\\n event_id: integer\\n section: string\\n base_price: float\\n current_price: float\\n demand_level: string\\n\\nEntity:\\n Attendee\\n id: integer\\n name: string\\n email: string\\n tickets: list\\n\\nBehavior:\\n CreateEvent\\n Input: name string, venue_id integer, date datetime, base_price float\\n Output: event Event\\n Action: Create event with venue assignment and generate seating inventory\\n\\nBehavior:\\n PurchaseTicket\\n Input: event_id integer, section string, row string, seat string, buyer_name string\\n Output: ticket Ticket\\n Action: Reserve seat, process payment, and issue ticket\\n\\nBehavior:\\n UpdatePricing\\n Input: event_id integer, demand_data dict\\n Output: updated_tiers list\\n Action: Adjust prices based on demand level and time to event\\n\\nBehavior:\\n ValidateTicket\\n Input: ticket_id integer, event_id integer\\n Output: validation dict\\n Action: Check ticket validity and mark as used\\n\\nBehavior:\\n TransferTicket\\n Input: ticket_id integer, new_holder_name string\\n Output: updated_ticket Ticket\\n Action: Transfer ticket to new holder with verification\\n\\nCondition:\\n When: tickets sold exceeds 80 percent of capacity\\n Then: Increase remaining ticket prices by surge factor\\n\\nCondition:\\n When: single user attempts to purchase more than 4 tickets\\n Then: Flag for bot detection and require additional verification\\n\\nCondition:\\n When: event is within 24 hours and tickets remain unsold\\n Then: Reduce prices to fill remaining capacity\\n\\nEvent:\\n On: TicketPurchased\\n Do: Update seating availability, adjust pricing, and send confirmation\\n\\nEvent:\\n On: EventCancelled\\n Do: Process refunds for all ticket holders and send cancellation notice\\n\\nEvent:\\n On: TicketValidated\\n Do: Mark ticket as used and update entry count\\n\\nParallel:\\n Events\\n Tickets\\n\\nOptimize:\\n Ticket purchase speed\\n Priority: Critical\\n\\nOptimize:\\n Pricing algorithm accuracy\\n Priority: High\"}

{\"id\": \"aicl_019\", \"domain\": \"veterinary_clinic\", \"levels\": [1,2,3,4,5], \"description\": \"Build a veterinary clinic system with pets, owners, appointments, and treatments\", \"code\": \"# Veterinary Clinic System\\n# Levels 1-5: Pet healthcare management\\n\\nGoal:\\nCreate a veterinary clinic system with pet records, owner management, appointment scheduling, treatment tracking, and vaccination reminders\\n\\nConstraint:\\nVaccination reminders must be sent 2 weeks before due date\\n\\nRisk:\\nPet allergic reaction to medication\\n\\nRecovery:\\nAdminister emergency treatment and document in pet record\\n\\nRisk:\\nAppointment no-show\\n\\nRecovery:\\nContact owner and offer rescheduling within 24 hours\\n\\nLayer:\\n Patients\\n SubLayer:\\n Pets\\n SubLayer:\\n Owners\\n\\nLayer:\\n Schedule\\n SubLayer:\\n Appointments\\n\\nLayer:\\n Medical\\n SubLayer:\\n Treatments\\n SubLayer:\\n Vaccinations\\n\\nValidation:\\nPets are registered with complete medical history\\n\\nValidation:\\nAppointments are scheduled without conflicts\\n\\nValidation:\\nVaccination reminders are sent on time\\n\\nEntity:\\n Pet\\n id: integer\\n name: string\\n species: string\\n breed: string\\n date_of_birth: datetime\\n weight: float\\n owner_id: integer\\n allergies: list\\n\\nEntity:\\n Owner\\n id: integer\\n name: string\\n email: string\\n phone: string\\n address: string\\n pets: list\\n\\nEntity:\\n Appointment\\n id: integer\\n pet_id: integer\\n vet_id: integer\\n datetime: datetime\\n reason: string\\n status: string\\n notes: string\\n\\nEntity:\\n Treatment\\n id: integer\\n pet_id: integer\\n date: datetime\\n diagnosis: string\\n procedure: string\\n medications: list\\n cost: float\\n\\nEntity:\\n Vaccination\\n id: integer\\n pet_id: integer\\n vaccine: string\\n date_administered: datetime\\n next_due: datetime\\n\\nBehavior:\\n RegisterPet\\n Input: name string, species string, breed string, owner_id integer\\n Output: pet Pet\\n Action: Create pet record with owner association\\n\\nBehavior:\\n ScheduleAppointment\\n Input: pet_id integer, vet_id integer, datetime datetime, reason string\\n Output: appointment Appointment\\n Action: Find available slot and book appointment\\n\\nBehavior:\\n RecordTreatment\\n Input: pet_id integer, diagnosis string, procedure string, medications list\\n Output: treatment Treatment\\n Action: Document treatment and update pet medical history\\n\\nBehavior:\\n AdministerVaccination\\n Input: pet_id integer, vaccine string\\n Output: vaccination Vaccination\\n Action: Record vaccination and schedule next due date\\n\\nBehavior:\\n SendReminder\\n Input: owner_id integer, message string, type string\\n Output: notification dict\\n Action: Send appointment or vaccination reminder to owner\\n\\nCondition:\\n When: vaccination is due within 14 days\\n Then: Send reminder to owner and flag in pet record\\n\\nCondition:\\n When: pet weight changes by more than 15 percent since last visit\\n Then: Flag for dietary consultation at next appointment\\n\\nEvent:\\n On: AppointmentBooked\\n Do: Send confirmation to owner and add to vet schedule\\n\\nEvent:\\n On: VaccinationDue\\n Do: Send reminder and offer scheduling options\"}

{\"id\": \"aicl_020\", \"domain\": \"game_server\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a multiplayer game server with players, matches, scores, leaderboards, and anti-cheat\", \"code\": \"# Multiplayer Game Server\\n# Levels 1-8: Game backend with ML anti-cheat\\n\\nGoal:\\nCreate a multiplayer game server with player management, matchmaking, match execution, scoring, leaderboards, and AI-powered anti-cheat detection\\n\\nConstraint:\\nMatchmaking must find opponents within 30 seconds\\n\\nConstraint:\\nGame state must sync at 60 ticks per second\\n\\nRisk:\\nPlayer using cheats or exploits\\n\\nRecovery:\\nFlag for review, shadow-ban suspicious player, and replay analysis\\n\\nRisk:\\nServer overload during peak hours\\n\\nRecovery:\\nSpin up additional server instances and queue incoming players\\n\\nRisk:\\nMatch desync between players\\n\\nRecovery:\\nRollback to last synchronized state and resimulate\\n\\nLayer:\\n Players\\n SubLayer:\\n Profiles\\n SubLayer:\\n Inventory\\n\\nLayer:\\n Matches\\n SubLayer:\\n Matchmaking\\n SubLayer:\\n Execution\\n\\nLayer:\\n Competition\\n SubLayer:\\n Leaderboards\\n SubLayer:\\n Seasons\\n\\nLayer:\\n Security\\n SubLayer:\\n AntiCheat\\n SubLayer:\\n Monitoring\\n\\nValidation:\\nPlayers can find and join matches quickly\\n\\nValidation:\\nScores and rankings are accurate and tamper-proof\\n\\nValidation:\\nAnti-cheat system detects violations\\n\\nEntity:\\n Player\\n id: integer\\n username: string\\n rank: string\\n rating: integer\\n wins: integer\\n losses: integer\\n created_at: datetime\\n is_banned: boolean\\n\\nEntity:\\n Match\\n id: integer\\n players: list\\n status: string\\n map: string\\n mode: string\\n started_at: datetime\\n duration: integer\\n winner_id: integer\\n\\nEntity:\\n Score\\n id: integer\\n match_id: integer\\n player_id: integer\\n points: integer\\n kills: integer\\n deaths: integer\\n assists: integer\\n\\nEntity:\\n LeaderboardEntry\\n player_id: integer\\n season_id: integer\\n rating: integer\\n rank: integer\\n wins: integer\\n losses: integer\\n\\nEntity:\\n CheatReport\\n id: integer\\n player_id: integer\\n reporter_id: integer\\n match_id: integer\\n type: string\\n evidence: dict\\n status: string\\n\\nBehavior:\\n RegisterPlayer\\n Input: username string, email string\\n Output: player Player\\n Action: Create player profile with default rank and rating\\n\\nBehavior:\\n FindMatch\\n Input: player_id integer, mode string, region string\\n Output: match Match\\n Action: Match player with similar-skilled opponents in region\\n\\nBehavior:\\n StartMatch\\n Input: players list, map string, mode string\\n Output: match Match\\n Action: Initialize game state and begin match simulation\\n\\nBehavior:\\n RecordScore\\n Input: match_id integer, player_id integer, stats dict\\n Output: score Score\\n Action: Record match performance and update player rating\\n\\nBehavior:\\n UpdateLeaderboard\\n Input: season_id integer, player_id integer, result string\\n Output: entry LeaderboardEntry\\n Action: Update seasonal leaderboard with match result\\n\\nBehavior:\\n DetectCheat\\n Input: player_id integer, match_data dict\\n Output: report CheatReport\\n Action: Analyze match data for anomalies and flag suspicious behavior\\n\\nCondition:\\n When: player win rate exceeds 85 percent over 50 matches\\n Then: Flag for anti-cheat review and increase monitoring level\\n\\nCondition:\\n When: matchmaking queue exceeds 60 seconds\\n Then: Expand skill range and region to find opponents\\n\\nCondition:\\n When: player reports exceed 5 in 24 hours\\n Then: Auto-suspend pending investigation\\n\\nEvent:\\n On: MatchCompleted\\n Do: Calculate ratings, update leaderboard, and distribute rewards\\n\\nEvent:\\n On: CheatDetected\\n Do: Log evidence, notify player, and apply sanctions\\n\\nEvent:\\n On: SeasonEnded\\n Do: Finalize rankings, distribute rewards, and reset for new season\\n\\nParallel:\\n Matches\\n Security\\n\\nOptimize:\\n Matchmaking speed\\n Priority: Critical\\n\\nOptimize:\\n Game state sync latency\\n Priority: Critical\\n\\nLearn:\\n cheat_detection\\n Based: performance_metrics, input_patterns, timing_analysis, hardware_fingerprint\\n Adapt: adjust detection thresholds based on false positive feedback\"}

{\"id\": \"aicl_021\", \"domain\": \"chess_game\", \"levels\": [1,2,3,4,5,6], \"description\": \"Build a chess game with board state, moves, validation, and game flow\", \"code\": \"# Chess Game\\n# Levels 1-6: Complete chess with concurrency\\n\\nGoal:\\nCreate a chess game with board state management, move validation, check and checkmate detection, game history, and player timer management\\n\\nConstraint:\\nMove validation must complete within 10 milliseconds\\n\\nConstraint:\\nTimer accuracy must be within 100 milliseconds\\n\\nRisk:\\nPlayer disconnects during game\\n\\nRecovery:\\nPause game and allow 5-minute reconnection window\\n\\nRisk:\\nInvalid move submitted\\n\\nRecovery:\\nReject move and notify player of legal alternatives\\n\\nLayer:\\n Game\\n SubLayer:\\n Board\\n SubLayer:\\n Rules\\n\\nLayer:\\n Players\\n SubLayer:\\n White\\n SubLayer:\\n Black\\n\\nLayer:\\n History\\n SubLayer:\\n Moves\\n SubLayer:\\n Positions\\n\\nValidation:\\nAll moves are validated against chess rules\\n\\nValidation:\\nCheck and checkmate are detected correctly\\n\\nValidation:\\nTimer counts down accurately\\n\\nEntity:\\n GameState\\n id: integer\\n board: dict\\n current_turn: string\\n move_count: integer\\n status: string\\n en_passant_target: string\\n castling_rights: dict\\n\\nEntity:\\n Player\\n id: integer\\n name: string\\n color: string\\n rating: integer\\n remaining_time: integer\\n\\nEntity:\\n Move\\n id: integer\\n game_id: integer\\n from_square: string\\n to_square: string\\n piece: string\\n captured: string\\n is_check: boolean\\n is_checkmate: boolean\\n notation: string\\n timestamp: datetime\\n\\nEntity:\\n ChessPiece\\n position: string\\n type: string\\n color: string\\n has_moved: boolean\\n\\nBehavior:\\n InitializeBoard\\n Input: variant string\\n Output: state GameState\\n Action: Set up standard chess starting position\\n\\nBehavior:\\n ValidateMove\\n Input: game_id integer, from_square string, to_square string\\n Output: valid boolean\\n Action: Check move against chess rules including check constraints\\n\\nBehavior:\\n MakeMove\\n Input: game_id integer, from_square string, to_square string, promotion string\\n Output: move Move\\n Action: Execute move, update board, and check game state\\n\\nBehavior:\\n DetectCheck\\n Input: game_id integer, color string\\n Output: in_check boolean\\n Action: Determine if the specified color king is in check\\n\\nBehavior:\\n DetectCheckmate\\n Input: game_id integer, color string\\n Output: is_checkmate boolean\\n Action: Check if color has no legal moves while in check\\n\\nBehavior:\\n UpdateTimer\\n Input: game_id integer, color string, elapsed integer\\n Output: remaining integer\\n Action: Decrement timer and flag if time expires\\n\\nCondition:\\n When: player timer reaches zero\\n Then: Player loses on time and game ends\\n\\nCondition:\\n When: position repeats 3 times\\n Then: Declare draw by threefold repetition\\n\\nCondition:\\n When: 50 moves occur without pawn move or capture\\n Then: Declare draw by fifty-move rule\\n\\nEvent:\\n On: MoveMade\\n Do: Update board display, switch turn, and check for check or checkmate\\n\\nEvent:\\n On: CheckmateDetected\\n Do: End game, update ratings, and record result\\n\\nEvent:\\n On: DrawOffered\\n Do: Present offer to opponent and handle response\\n\\nParallel:\\n Game\\n Players\"}

{\"id\": \"aicl_022\", \"domain\": \"parking_system\", \"levels\": [1,2,3,4,5], \"description\": \"Build a smart parking system with spots, reservations, payments, and occupancy tracking\", \"code\": \"# Smart Parking System\\n# Levels 1-5: Urban parking management\\n\\nGoal:\\nCreate a smart parking system with spot management, reservation booking, payment processing, occupancy tracking, and dynamic pricing\\n\\nConstraint:\\nSpot availability must update within 5 seconds of change\\n\\nRisk:\\nVehicle overstays reservation\\n\\nRecovery:\\nCharge overtime rate and notify driver via app\\n\\nRisk:\\nSensor malfunction gives false availability\\n\\nRecovery:\\nFlag spot for manual verification and mark as unavailable\\n\\nLayer:\\n Parking\\n SubLayer:\\n Spots\\n SubLayer:\\n Sensors\\n\\nLayer:\\n Reservations\\n SubLayer:\\n Booking\\n SubLayer:\\n Payment\\n\\nValidation:\\nSpot availability is accurate and up-to-date\\n\\nValidation:\\nReservations are processed correctly\\n\\nEntity:\\n ParkingSpot\\n id: integer\\n zone: string\\n level: integer\\n number: string\\n type: string\\n is_occupied: boolean\\n hourly_rate: float\\n last_updated: datetime\\n\\nEntity:\\n Reservation\\n id: integer\\n spot_id: integer\\n vehicle_plate: string\\n start_time: datetime\\n end_time: datetime\\n status: string\\n total_cost: float\\n\\nEntity:\\n Vehicle\\n plate: string\\n make: string\\n color: string\\n owner_id: integer\\n\\nEntity:\\n Payment\\n id: integer\\n reservation_id: integer\\n amount: float\\n method: string\\n status: string\\n processed_at: datetime\\n\\nBehavior:\\n FindSpot\\n Input: zone string, type string, start_time datetime, duration integer\\n Output: spot ParkingSpot\\n Action: Search available spots matching criteria\\n\\nBehavior:\\n ReserveSpot\\n Input: spot_id integer, vehicle_plate string, start_time datetime, end_time datetime\\n Output: reservation Reservation\\n Action: Book spot and process prepayment\\n\\nBehavior:\\n ProcessPayment\\n Input: reservation_id integer, method string\\n Output: payment Payment\\n Action: Calculate and charge parking fee\\n\\nBehavior:\\n UpdateOccupancy\\n Input: spot_id integer, is_occupied boolean\\n Output: updated_spot ParkingSpot\\n Action: Update spot occupancy from sensor data\\n\\nCondition:\\n When: parking zone occupancy exceeds 90 percent\\n Then: Increase hourly rate by surge factor\\n\\nCondition:\\n When: vehicle overstays reservation by more than 15 minutes\\n Then: Charge overtime rate at 150 percent of standard rate\\n\\nEvent:\\n On: SpotOccupied\\n Do: Update availability display and confirm reservation\\n\\nEvent:\\n On: ReservationExpiring\\n Do: Send notification to driver 15 minutes before end time\"}

{\"id\": \"aicl_023\", \"domain\": \"crop_farming\", \"levels\": [1,2,3,4,5,7,8], \"description\": \"Build a smart farming system with crops, fields, irrigation, and yield prediction\", \"code\": \"# Smart Crop Farming System\\n# Levels 1-5, 7-8: Agricultural management with ML\\n\\nGoal:\\nCreate a smart farming system with crop planning, field management, irrigation control, harvest tracking, and yield prediction\\n\\nConstraint:\\nIrrigation must respond to soil moisture within 30 minutes\\n\\nConstraint:\\nWeather alerts must be processed within 5 minutes\\n\\nRisk:\\nFrost damage to crops\\n\\nRecovery:\\nActivate frost protection irrigation and deploy covers\\n\\nRisk:\\nPest or disease outbreak\\n\\nRecovery:\\nIsolate affected area and apply targeted treatment\\n\\nRisk:\\nIrrigation system failure\\n\\nRecovery:\\nSwitch to backup water source and alert maintenance\\n\\nLayer:\\n Fields\\n SubLayer:\\n Zones\\n SubLayer:\\n Sensors\\n\\nLayer:\\n Crops\\n SubLayer:\\n Planting\\n SubLayer:\\n Growth\\n\\nLayer:\\n Irrigation\\n SubLayer:\\n Control\\n SubLayer:\\n Scheduling\\n\\nValidation:\\nField conditions are monitored continuously\\n\\nValidation:\\nIrrigation activates based on soil moisture thresholds\\n\\nValidation:\\nYield predictions improve over time\\n\\nEntity:\\n Field\\n id: integer\\n name: string\\n area_hectares: float\\n soil_type: string\\n current_crop: string\\n zone_count: integer\\n\\nEntity:\\n Crop\\n id: integer\\n name: string\\n variety: string\\n planting_date: datetime\\n expected_harvest: datetime\\n growth_stage: string\\n field_id: integer\\n\\nEntity:\\n SensorReading\\n id: integer\\n field_id: integer\\n sensor_type: string\\n value: float\\n unit: string\\n timestamp: datetime\\n\\nEntity:\\n IrrigationZone\\n id: integer\\n field_id: integer\\n name: string\\n flow_rate: float\\n is_active: boolean\\n last_activated: datetime\\n\\nEntity:\\n Harvest\\n id: integer\\n crop_id: integer\\n yield_amount: float\\n quality_grade: string\\n harvest_date: datetime\\n\\nBehavior:\\n PlantCrop\\n Input: field_id integer, crop_name string, variety string, planting_date datetime\\n Output: crop Crop\\n Action: Register crop planting in field and schedule growth milestones\\n\\nBehavior:\\n MonitorConditions\\n Input: field_id integer\\n Output: conditions dict\\n Action: Aggregate sensor readings for soil moisture, temperature, and humidity\\n\\nBehavior:\\n ActivateIrrigation\\n Input: zone_id integer, duration_minutes integer\\n Output: activation dict\\n Action: Turn on irrigation zone for specified duration\\n\\nBehavior:\\n RecordHarvest\\n Input: crop_id integer, yield_amount float, quality string\\n Output: harvest Harvest\\n Action: Log harvest results and update field status\\n\\nBehavior:\\n PredictYield\\n Input: crop_id integer, conditions dict\\n Output: prediction dict\\n Action: Forecast expected yield based on conditions and growth stage\\n\\nCondition:\\n When: soil moisture drops below 30 percent\\n Then: Activate irrigation for affected zone for 30 minutes\\n\\nCondition:\\n When: frost warning issued for area\\n Then: Activate all irrigation zones for frost protection\\n\\nCondition:\\n When: pest detection threshold exceeded in zone\\n Then: Alert agronomist and recommend targeted treatment\\n\\nEvent:\\n On: SensorReadingReceived\\n Do: Update field dashboard and check irrigation thresholds\\n\\nEvent:\\n On: GrowthStageChanged\\n Do: Adjust irrigation schedule and update nutrient plan\\n\\nEvent:\\n On: WeatherAlertReceived\\n Do: Evaluate crop risk and activate protective measures\\n\\nOptimize:\\n Irrigation water usage\\n Priority: High\\n\\nOptimize:\\n Crop yield per hectare\\n Priority: High\\n\\nLearn:\\n yield_prediction\\n Based: weather_history, soil_conditions, planting_date, variety, field_location\\n Adapt: refine yield estimates based on actual vs predicted harvests\"}

{\"id\": \"aicl_024\", \"domain\": \"chat_app\", \"levels\": [1,2,3,4,5,6,7,8,9], \"description\": \"Build a real-time chat application with users, messages, channels, encryption, and smart replies\", \"code\": \"# Real-Time Chat Application\\n# Levels 1-9: Messaging platform with security and AI\\n\\nGoal:\\nCreate a real-time chat application with user management, direct messaging, group channels, message encryption, file sharing, and AI-powered smart replies\\n\\nConstraint:\\nMessage delivery latency must be under 500 milliseconds\\n\\nConstraint:\\nEnd-to-end encryption for private messages\\n\\nRisk:\\nMessage delivery failure\\n\\nRecovery:\\nQueue message for retry and notify sender of delayed delivery\\n\\nRisk:\\nChannel spam or abuse\\n\\nRecovery:\\nRate-limit user and flag for moderator review\\n\\nRisk:\\nFile sharing malware\\n\\nRecovery:\\nScan file before delivery and quarantine if threat detected\\n\\nLayer:\\n Users\\n SubLayer:\\n Profiles\\n SubLayer:\\n Status\\n\\nLayer:\\n Messages\\n SubLayer:\\n Direct\\n SubLayer:\\n Channels\\n\\nLayer:\\n Media\\n SubLayer:\\n Files\\n SubLayer:\\n Links\\n\\nLayer:\\n Security\\n SubLayer:\\n Encryption\\n SubLayer:\\n Moderation\\n\\nValidation:\\nMessages are delivered in real-time\\n\\nValidation:\\nPrivate messages are end-to-end encrypted\\n\\nValidation:\\nFile sharing scans for threats\\n\\nEntity:\\n User\\n id: integer\\n username: string\\n display_name: string\\n email: string\\n status: string\\n last_seen: datetime\\n avatar_url: string\\n\\nEntity:\\n Message\\n id: integer\\n sender_id: integer\\n channel_id: integer\\n content: string\\n type: string\\n created_at: datetime\\n edited: boolean\\n reactions: dict\\n\\nEntity:\\n Channel\\n id: integer\\n name: string\\n type: string\\n members: list\\n created_at: datetime\\n topic: string\\n\\nEntity:\\n FileAttachment\\n id: integer\\n message_id: integer\\n filename: string\\n url: string\\n size: integer\\n mime_type: string\\n scanned: boolean\\n\\nEntity:\\n ModerationAction\\n id: integer\\n target_user_id: integer\\n moderator_id: integer\\n action: string\\n reason: string\\n created_at: datetime\\n\\nBehavior:\\n SendMessage\\n Input: sender_id integer, channel_id integer, content string, type string\\n Output: message Message\\n Action: Create message, encrypt if private, and broadcast to recipients\\n\\nBehavior:\\n CreateChannel\\n Input: name string, type string, members list\\n Output: channel Channel\\n Action: Create channel and add initial members\\n\\nBehavior:\\n JoinChannel\\n Input: user_id integer, channel_id integer\\n Output: membership dict\\n Action: Add user to channel and broadcast join notification\\n\\nBehavior:\\n ShareFile\\n Input: sender_id integer, channel_id integer, file dict\\n Output: attachment FileAttachment\\n Action: Scan file, store, and share with message\\n\\nBehavior:\\n ModerateUser\\n Input: target_id integer, moderator_id integer, action string, reason string\\n Output: moderation ModerationAction\\n Action: Apply moderation action and notify user\\n\\nBehavior:\\n GenerateSmartReply\\n Input: message_id integer, context list\\n Output: suggestions list\\n Action: Suggest quick replies based on message context\\n\\nCondition:\\n When: user sends more than 10 messages per second\\n Then: Rate-limit user and warn about spam policy\\n\\nCondition:\\n When: file scan detects malware\\n Then: Block file delivery and quarantine, notify sender and channel admin\\n\\nCondition:\\n When: channel has no activity for 90 days\\n Then: Archive channel and notify members\\n\\nEvent:\\n On: MessageSent\\n Do: Deliver to recipients, update unread counts, and trigger smart reply\\n\\nEvent:\\n On: UserJoined\\n Do: Send welcome message and update member list\\n\\nEvent:\\n On: FileShared\\n Do: Scan file and deliver if safe\\n\\nParallel:\\n Messages\\n Media\\n\\nOptimize:\\n Message delivery latency\\n Priority: Critical\\n\\nOptimize:\\n Search indexing speed\\n Priority: High\\n\\nLearn:\\n smart_reply\\n Based: conversation_context, user_style, message_sentiment, response_patterns\\n Adapt: personalize suggestions based on user acceptance rate\\n\\nEncrypt:\\n message content for private channels\\n file attachments for private channels\\n\\nProtect:\\n User data from unauthorized access\\n Messages from interception\\n Files from malware\"}

{\"id\": \"aicl_025\", \"domain\": \"employee_hr\", \"levels\": [1,2,3,4,5,7], \"description\": \"Build an HR management system with employees, departments, payroll, leave, and performance reviews\", \"code\": \"# HR Management System\\n# Levels 1-5, 7: Human resources platform\\n\\nGoal:\\nCreate an HR management system with employee records, department management, payroll processing, leave management, and performance reviews\\n\\nConstraint:\\nPayroll must be processed on the 1st and 15th of each month without fail\\n\\nConstraint:\\nLeave balance must be accurate to the hour\\n\\nRisk:\\nPayroll calculation error\\n\\nRecovery:\\nRecalculate from source data and flag discrepancy for HR review\\n\\nRisk:\\nEmployee data breach\\n\\nRecovery:\\nLock affected records, notify security team, and comply with breach notification laws\\n\\nRisk:\\nLeave approval conflict with team coverage\\n\\nRecovery:\\nDetect minimum staffing violation and suggest alternative dates\\n\\nLayer:\\n Employees\\n SubLayer:\\n Records\\n SubLayer:\\n Documents\\n\\nLayer:\\n Organization\\n SubLayer:\\n Departments\\n SubLayer:\\n Teams\\n\\nLayer:\\n Compensation\\n SubLayer:\\n Payroll\\n SubLayer:\\n Benefits\\n\\nLayer:\\n TimeOff\\n SubLayer:\\n Leave\\n SubLayer:\\n Attendance\\n\\nValidation:\\nEmployee records are complete and up-to-date\\n\\nValidation:\\nPayroll is calculated accurately including deductions\\n\\nValidation:\\nLeave balances are tracked correctly\\n\\nEntity:\\n Employee\\n id: integer\\n name: string\\n email: string\\n department_id: integer\\n position: string\\n hire_date: datetime\\n salary: float\\n status: string\\n manager_id: integer\\n\\nEntity:\\n Department\\n id: integer\\n name: string\\n head_id: integer\\n budget: float\\n headcount: integer\\n\\nEntity:\\n PayrollRecord\\n id: integer\\n employee_id: integer\\n period: string\\n base_salary: float\\n deductions: float\\n bonus: float\\n net_pay: float\\n processed_at: datetime\\n\\nEntity:\\n LeaveRequest\\n id: integer\\n employee_id: integer\\n type: string\\n start_date: datetime\\n end_date: datetime\\n status: string\\n approver_id: integer\\n reason: string\\n\\nEntity:\\n PerformanceReview\\n id: integer\\n employee_id: integer\\n reviewer_id: integer\\n rating: float\\n goals: list\\n feedback: string\\n period: string\\n completed_at: datetime\\n\\nBehavior:\\n OnboardEmployee\\n Input: name string, email string, department_id integer, position string, salary float\\n Output: employee Employee\\n Action: Create employee record and initiate onboarding workflow\\n\\nBehavior:\\n ProcessPayroll\\n Input: period string, department_id integer\\n Output: records list\\n Action: Calculate salaries, deductions, and bonuses for all employees\\n\\nBehavior:\\n RequestLeave\\n Input: employee_id integer, type string, start_date datetime, end_date datetime, reason string\\n Output: request LeaveRequest\\n Action: Submit leave request and check team coverage\\n\\nBehavior:\\n ApproveLeave\\n Input: request_id integer, approver_id integer\\n Output: updated_request LeaveRequest\\n Action: Approve leave and update employee balance\\n\\nBehavior:\\n SubmitReview\\n Input: employee_id integer, reviewer_id integer, rating float, feedback string, goals list\\n Output: review PerformanceReview\\n Action: Record performance review and update employee goals\\n\\nCondition:\\n When: employee has less than 2 days of leave remaining\\n Then: Flag for manager awareness when new leave is requested\\n\\nCondition:\\n When: department headcount exceeds budget allocation\\n Then: Alert HR director and freeze new hiring\\n\\nCondition:\\n When: payroll net_pay differs from previous period by more than 20 percent\\n Then: Flag for manual review before processing\\n\\nEvent:\\n On: EmployeeOnboarded\\n Do: Create accounts, assign equipment, and schedule orientation\\n\\nEvent:\\n On: PayrollProcessed\\n Do: Send pay stubs to employees and update accounting records\\n\\nEvent:\\n On: LeaveApproved\\n Do: Update team calendar and notify coverage team\\n\\nOptimize:\\n Payroll processing speed\\n Priority: High\\n\\nOptimize:\\n Leave balance accuracy\\n Priority: High\"}

{\"id\": \"aicl_026\", \"domain\": \"podcast_platform\", \"levels\": [1,2,3,4,5,7,8], \"description\": \"Build a podcast platform with shows, episodes, subscriptions, and content recommendations\", \"code\": \"# Podcast Platform\\n# Levels 1-5, 7-8: Audio content platform with ML\\n\\nGoal:\\nCreate a podcast platform with show management, episode publishing, subscription handling, playback, and AI-powered content recommendations\\n\\nConstraint:\\nNew episodes must be available to subscribers within 1 minute of publish\\n\\nConstraint:\\nPlayback must start within 3 seconds\\n\\nRisk:\\nAudio file corruption\\n\\nRecovery:\\nFall back to backup encoding and alert content team\\n\\nRisk:\\nSubscription renewal failure\\n\\nRecovery:\\nRetry payment and provide 3-day grace period\\n\\nRisk:\\nRecommendation algorithm bias\\n\\nRecovery:\\nInject 20 percent diverse content and monitor satisfaction metrics\\n\\nLayer:\\n Content\\n SubLayer:\\n Shows\\n SubLayer:\\n Episodes\\n\\nLayer:\\n Users\\n SubLayer:\\n Profiles\\n SubLayer:\\n Subscriptions\\n\\nLayer:\\n Playback\\n SubLayer:\\n Streaming\\n SubLayer:\\n History\\n\\nValidation:\\nEpisodes are published and distributed correctly\\n\\nValidation:\\nPlayback works across all supported devices\\n\\nValidation:\\nRecommendations improve with listening history\\n\\nEntity:\\n Show\\n id: integer\\n title: string\\n host: string\\n description: string\\n category: string\\n episode_count: integer\\n subscriber_count: integer\\n cover_url: string\\n\\nEntity:\\n Episode\\n id: integer\\n show_id: integer\\n title: string\\n description: string\\n duration: integer\\n audio_url: string\\n published_at: datetime\\n play_count: integer\\n\\nEntity:\\n User\\n id: integer\\n username: string\\n email: string\\n subscriptions: list\\n listening_history: list\\n created_at: datetime\\n\\nEntity:\\n Subscription\\n id: integer\\n user_id: integer\\n show_id: integer\\n subscribed_at: datetime\\n notifications_enabled: boolean\\n\\nEntity:\\n PlayHistory\\n id: integer\\n user_id: integer\\n episode_id: integer\\n position: integer\\n completed: boolean\\n played_at: datetime\\n\\nBehavior:\\n PublishEpisode\\n Input: show_id integer, title string, description string, audio_url string\\n Output: episode Episode\\n Action: Publish episode and notify all subscribers\\n\\nBehavior:\\n SubscribeToShow\\n Input: user_id integer, show_id integer\\n Output: subscription Subscription\\n Action: Add show to user subscriptions and enable notifications\\n\\nBehavior:\\n StreamEpisode\\n Input: episode_id integer, user_id integer\\n Output: stream_url string\\n Action: Authenticate and return streaming URL with resume position\\n\\nBehavior:\\n RecordPlayProgress\\n Input: user_id integer, episode_id integer, position integer\\n Output: history PlayHistory\\n Action: Save playback position for resume capability\\n\\nBehavior:\\n GetRecommendations\\n Input: user_id integer, count integer\\n Output: shows list\\n Action: Suggest shows based on listening history and preferences\\n\\nCondition:\\n When: episode audio file exceeds 500 MB\\n Then: Apply additional compression and warn about quality reduction\\n\\nCondition:\\n When: user has not listened in 30 days\\n Then: Send re-engagement email with personalized recommendations\\n\\nEvent:\\n On: EpisodePublished\\n Do: Notify subscribers, update RSS feed, and index for search\\n\\nEvent:\\n On: EpisodeCompleted\\n Do: Mark as listened, update recommendations, and suggest next episode\\n\\nEvent:\\n On: NewSubscriber\\n Do: Update show subscriber count and send welcome message\\n\\nOptimize:\\n Audio streaming latency\\n Priority: Critical\\n\\nOptimize:\\n Recommendation relevance\\n Priority: High\\n\\nLearn:\\n content_recommendation\\n Based: listening_history, subscription_genre, completion_rate, listening_time\\n Adapt: weight recommendation factors based on skip rate and subscription conversions\"}

{\"id\": \"aicl_027\", \"domain\": \"car_rental\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a car rental system with fleet, reservations, payments, and vehicle tracking\", \"code\": \"# Car Rental System\\n# Levels 1-7: Fleet management with pricing\\n\\nGoal:\\nCreate a car rental system with fleet management, reservation booking, vehicle handover, return processing, dynamic pricing, and maintenance tracking\\n\\nConstraint:\\nVehicle availability must update in real-time\\n\\nConstraint:\\nReservation confirmation must be sent within 30 seconds\\n\\nRisk:\\nVehicle returned with damage\\n\\nRecovery:\\nDocument damage with photos, charge repair fee, and update vehicle condition\\n\\nRisk:\\nCustomer returns vehicle late\\n\\nRecovery:\\nCharge late fee and adjust next reservation start time\\n\\nRisk:\\nVehicle breakdown during rental\\n\\nRecovery:\\nDispatch roadside assistance and provide replacement vehicle\\n\\nLayer:\\n Fleet\\n SubLayer:\\n Vehicles\\n SubLayer:\\n Maintenance\\n\\nLayer:\\n Reservations\\n SubLayer:\\n Booking\\n SubLayer:\\n Pricing\\n\\nLayer:\\n Operations\\n SubLayer:\\n Handover\\n SubLayer:\\n Return\\n\\nValidation:\\nVehicles can be reserved and tracked correctly\\n\\nValidation:\\nDynamic pricing adjusts to demand\\n\\nValidation:\\nMaintenance is scheduled proactively\\n\\nEntity:\\n Vehicle\\n id: integer\\n make: string\\n model: string\\n year: integer\\n license_plate: string\\n category: string\\n daily_rate: float\\n mileage: integer\\n status: string\\n condition: string\\n\\nEntity:\\n Reservation\\n id: integer\\n customer_id: integer\\n vehicle_id: integer\\n pickup_location: string\\n dropoff_location: string\\n start_date: datetime\\n end_date: datetime\\n total_cost: float\\n status: string\\n\\nEntity:\\n Customer\\n id: integer\\n name: string\\n email: string\\n phone: string\\n license_number: string\\n license_valid: boolean\\n\\nEntity:\\n MaintenanceRecord\\n id: integer\\n vehicle_id: integer\\n type: string\\n description: string\\n cost: float\\n performed_at: datetime\\n next_due_mileage: integer\\n\\nBehavior:\\n ReserveVehicle\\n Input: customer_id integer, vehicle_id integer, start_date datetime, end_date datetime\\n Output: reservation Reservation\\n Action: Verify availability and create reservation with pricing\\n\\nBehavior:\\n HandoverVehicle\\n Input: reservation_id integer, condition_report dict\\n Output: handover dict\\n Action: Record vehicle condition and confirm customer possession\\n\\nBehavior:\\n ProcessReturn\\n Input: reservation_id integer, return_condition dict, mileage integer\\n Output: return_receipt dict\\n Action: Inspect vehicle, calculate charges, and update availability\\n\\nBehavior:\\n ScheduleMaintenance\\n Input: vehicle_id integer, type string, description string\\n Output: record MaintenanceRecord\\n Action: Schedule maintenance and mark vehicle as unavailable\\n\\nBehavior:\\n CalculatePricing\\n Input: vehicle_category string, duration integer, season string, demand float\\n Output: price float\\n Action: Compute rental price with dynamic adjustments\\n\\nCondition:\\n When: vehicle mileage approaches maintenance threshold\\n Then: Schedule preventive maintenance before next rental\\n\\nCondition:\\n When: peak season demand exceeds fleet capacity\\n Then: Increase daily rates and offer upgrade alternatives\\n\\nCondition:\\n When: customer license is expired\\n Then: Block reservation and notify customer to renew license\\n\\nEvent:\\n On: ReservationCreated\\n Do: Send confirmation and block vehicle availability\\n\\nEvent:\\n On: VehicleReturned\\n Do: Process charges, update mileage, and check maintenance schedule\\n\\nEvent:\\n On: MaintenanceDue\\n Do: Remove vehicle from availability and schedule service appointment\\n\\nParallel:\\n Fleet\\n Reservations\\n\\nOptimize:\\n Fleet utilization rate\\n Priority: High\\n\\nOptimize:\\n Dynamic pricing accuracy\\n Priority: High\"}

{\"id\": \"aicl_028\", \"domain\": \"voting_system\", \"levels\": [1,2,3,4,9], \"description\": \"Build a secure electronic voting system with voters, ballots, candidates, and audit trails\", \"code\": \"# Secure Electronic Voting System\\n# Levels 1-4, 9: Elections with maximum security\\n\\nGoal:\\nCreate a secure electronic voting system with voter verification, ballot casting, vote tallying, candidate management, and cryptographic audit trails\\n\\nConstraint:\\nVotes must be anonymous and unverifiable to any third party\\n\\nConstraint:\\nTally results must be cryptographically verifiable\\n\\nConstraint:\\nSystem must be available 99.99 percent during voting period\\n\\nRisk:\\nVoter identity linked to cast ballot\\n\\nRecovery:\\nImplement zero-knowledge proof verification and audit privacy breach\\n\\nRisk:\\nBallot stuffing or duplicate voting\\n\\nRecovery:\\nReject duplicate vote, log attempt, and alert election officials\\n\\nRisk:\\nTally manipulation\\n\\nRecovery:\\nRe-run tally from encrypted ballots and compare with published results\\n\\nLayer:\\n Voters\\n SubLayer:\\n Registration\\n SubLayer:\\n Verification\\n\\nLayer:\\n Ballots\\n SubLayer:\\n Casting\\n SubLayer:\\n Storage\\n\\nLayer:\\n Results\\n SubLayer:\\n Tallying\\n SubLayer:\\n Audit\\n\\nValidation:\\nEach eligible voter can cast exactly one ballot\\n\\nValidation:\\nVote tally matches encrypted ballot count\\n\\nValidation:\\nSystem prevents double voting\\n\\nEntity:\\n Voter\\n id: integer\\n name: string\\n district: string\\n is_verified: boolean\\n has_voted: boolean\\n\\nEntity:\\n Candidate\\n id: integer\\n name: string\\n party: string\\n position: string\\n vote_count: integer\\n\\nEntity:\\n Ballot\\n id: integer\\n election_id: integer\\n selections: dict\\n timestamp: datetime\\n hash: string\\n\\nEntity:\\n Election\\n id: integer\\n name: string\\n start_time: datetime\\n end_time: datetime\\n status: string\\n total_votes: integer\\n\\nBehavior:\\n VerifyVoter\\n Input: voter_id integer, verification_data dict\\n Output: verified boolean\\n Action: Verify voter identity without linking to ballot choices\\n\\nBehavior:\\n CastBallot\\n Input: voter_id integer, election_id integer, selections dict\\n Output: confirmation dict\\n Action: Encrypt and store ballot selections, mark voter as voted\\n\\nBehavior:\\n TallyVotes\\n Input: election_id integer\\n Output: results dict\\n Action: Decrypt and count all ballots for the election\\n\\nBehavior:\\n VerifyTally\\n Input: election_id integer, published_results dict\\n Output: verified boolean\\n Action: Cryptographically verify that tally matches encrypted ballots\\n\\nCondition:\\n When: voter attempts to cast second ballot\\n Then: Reject ballot and log security incident\\n\\nCondition:\\n When: election end time is reached\\n Then: Close ballot casting and begin tally process\\n\\nEvent:\\n On: BallotCast\\n Do: Store encrypted ballot and send anonymous confirmation to voter\\n\\nEvent:\\n On: ElectionClosed\\n Do: Begin vote tally and prepare audit trail\\n\\nEncrypt:\\n ballot selections\\n voter verification_data\\n\\nProtect:\\n Ballot anonymity from deanonymization attacks\\n Tally integrity from manipulation\\n Voter records from unauthorized access\"}

{\"id\": \"aicl_029\", \"domain\": \"laundry_service\", \"levels\": [1,2,3,4], \"description\": \"Build a laundry service with orders, pickup, cleaning, and delivery\", \"code\": \"# Laundry Service Platform\\n# Levels 1-4: Simple service management\\n\\nGoal:\\nCreate a laundry service platform with order management, pickup scheduling, cleaning tracking, and delivery coordination\\n\\nConstraint:\\nPickup must occur within 2 hours of order\\n\\nRisk:\\nGarment damaged during cleaning\\n\\nRecovery:\\nReimburse customer at declared value and improve process for item type\\n\\nRisk:\\nPickup or delivery delay\\n\\nRecovery:\\nNotify customer with updated ETA and offer discount on next order\\n\\nLayer:\\n Orders\\n SubLayer:\\n Placement\\n SubLayer:\\n Tracking\\n\\nLayer:\\n Service\\n SubLayer:\\n Cleaning\\n SubLayer:\\n Quality\\n\\nValidation:\\nOrders progress through all stages correctly\\n\\nValidation:\\nGarment care instructions are followed\\n\\nEntity:\\n Order\\n id: integer\\n customer_id: integer\\n items: list\\n status: string\\n total: float\\n placed_at: datetime\\n estimated_delivery: datetime\\n\\nEntity:\\n Garment\\n id: integer\\n type: string\\n fabric: string\\n care_instructions: string\\n declared_value: float\\n status: string\\n\\nEntity:\\n Customer\\n id: integer\\n name: string\\n address: string\\n phone: string\\n preferences: dict\\n\\nBehavior:\\n PlaceOrder\\n Input: customer_id integer, items list, pickup_time datetime\\n Output: order Order\\n Action: Create order with items and schedule pickup\\n\\nBehavior:\\n ProcessGarment\\n Input: garment_id integer, process_type string\\n Output: updated_garment Garment\\n Action: Apply cleaning process according to care instructions\\n\\nBehavior:\\n QualityCheck\\n Input: order_id integer\\n Output: passed boolean\\n Action: Verify all items meet quality standards before delivery\\n\\nCondition:\\n When: garment care instructions specify dry clean only\\n Then: Route to dry cleaning process and skip standard wash\\n\\nEvent:\\n On: OrderPlaced\\n Do: Schedule pickup and send confirmation\\n\\nEvent:\\n On: CleaningComplete\\n Do: Run quality check and prepare for delivery\"}

{\"id\": \"aicl_030\", \"domain\": \"conference_organizer\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a conference organizer with sessions, speakers, attendees, scheduling, and live streaming\", \"code\": \"# Conference Organizer Platform\\n# Levels 1-7: Event management with parallel tracks\\n\\nGoal:\\nCreate a conference organizer with session scheduling, speaker management, attendee registration, venue coordination, and live streaming\\n\\nConstraint:\\nSession scheduling must prevent speaker time conflicts\\n\\nConstraint:\\nLive stream must have less than 5 second delay\\n\\nRisk:\\nSpeaker cancels at last minute\\n\\nRecovery:\\nFind replacement speaker or convert to panel discussion\\n\\nRisk:\\nSession room over capacity\\n\\nRecovery:\\nOpen overflow room with live stream and monitor headcount\\n\\nRisk:\\nLive stream technical failure\\n\\nRecovery:\\nSwitch to backup stream server and notify virtual attendees\\n\\nLayer:\\n Program\\n SubLayer:\\n Sessions\\n SubLayer:\\n Tracks\\n\\nLayer:\\n People\\n SubLayer:\\n Speakers\\n SubLayer:\\n Attendees\\n\\nLayer:\\n Venue\\n SubLayer:\\n Rooms\\n SubLayer:\\n Equipment\\n\\nLayer:\\n Virtual\\n SubLayer:\\n Streaming\\n SubLayer:\\n Recordings\\n\\nValidation:\\nSessions are scheduled without speaker or room conflicts\\n\\nValidation:\\nAttendees can register and select sessions\\n\\nValidation:\\nLive streaming works reliably\\n\\nEntity:\\n Session\\n id: integer\\n title: string\\n speaker_id: integer\\n room_id: integer\\n track: string\\n start_time: datetime\\n duration: integer\\n capacity: integer\\n registered_count: integer\\n\\nEntity:\\n Speaker\\n id: integer\\n name: string\\n bio: string\\n company: string\\n sessions: list\\n\\nEntity:\\n Attendee\\n id: integer\\n name: string\\n email: string\\n ticket_type: string\\n selected_sessions: list\\n\\nEntity:\\n Room\\n id: integer\\n name: string\\n capacity: integer\\n equipment: list\\n is_available: boolean\\n\\nEntity:\\n Stream\\n id: integer\\n session_id: integer\\n url: string\\n viewer_count: integer\\n status: string\\n\\nBehavior:\\n ScheduleSession\\n Input: title string, speaker_id integer, room_id integer, start_time datetime, duration integer\\n Output: session Session\\n Action: Check conflicts and create session in schedule\\n\\nBehavior:\\n RegisterAttendee\\n Input: name string, email string, ticket_type string\\n Output: attendee Attendee\\n Action: Register attendee and send confirmation\\n\\nBehavior:\\n SelectSession\\n Input: attendee_id integer, session_id integer\\n Output: selection dict\\n Action: Add session to attendee schedule if capacity allows\\n\\nBehavior:\\n StartStream\\n Input: session_id integer\\n Output: stream Stream\\n Action: Initialize live stream for session\\n\\nBehavior:\\n RecordSession\\n Input: session_id integer, recording_url string\\n Output: recording dict\\n Action: Save session recording for on-demand access\\n\\nCondition:\\n When: session registered_count reaches capacity\\n Then: Mark session as full and redirect to waitlist\\n\\nCondition:\\n When: speaker has overlapping sessions\\n Then: Reject scheduling and suggest alternative time\\n\\nCondition:\\n When: stream viewer_count exceeds plan limit\\n Then: Upgrade stream capacity and notify operations team\\n\\nEvent:\\n On: SessionStarted\\n Do: Activate live stream, open room, and notify registered attendees\\n\\nEvent:\\n On: SessionEnded\\n Do: Stop stream, save recording, and collect feedback\\n\\nEvent:\\n On: AttendeeRegistered\\n Do: Send welcome package and session selection guide\\n\\nParallel:\\n Program\\n Virtual\\n\\nOptimize:\\n Schedule conflict detection\\n Priority: Critical\\n\\nOptimize:\\n Stream latency\\n Priority: High\"}

{\"id\": \"aicl_031\", \"domain\": \"subscription_box\", \"levels\": [1,2,3,4,5,7], \"description\": \"Build a subscription box service with products, boxes, shipments, and customer preferences\", \"code\": \"# Subscription Box Service\\n# Levels 1-5, 7: Curated delivery platform\\n\\nGoal:\\nCreate a subscription box service with product curation, box assembly, shipment management, customer preferences, and billing\\n\\nConstraint:\\nBoxes must be shipped by the 5th of each month\\n\\nConstraint:\\nCustomer preferences must be applied to at least 80 percent of box contents\\n\\nRisk:\\nProduct out of stock for box assembly\\n\\nRecovery:\\nSubstitute with equivalent product and notify customer of change\\n\\nRisk:\\nShipment lost in transit\\n\\nRecovery:\\nReship box with expedited shipping and file carrier claim\\n\\nRisk:\\nCustomer cancellation spike after pricing change\\n\\nRecovery:\\nOffer retention discount and analyze cancellation feedback\\n\\nLayer:\\n Products\\n SubLayer:\\n Catalog\\n SubLayer:\\n Inventory\\n\\nLayer:\\n Boxes\\n SubLayer:\\n Curation\\n SubLayer:\\n Assembly\\n\\nLayer:\\n Customers\\n SubLayer:\\n Profiles\\n SubLayer:\\n Preferences\\n\\nLayer:\\n Shipping\\n SubLayer:\\n Fulfillment\\n SubLayer:\\n Tracking\\n\\nValidation:\\nBoxes are curated based on customer preferences\\n\\nValidation:\\nShipments are delivered on schedule\\n\\nValidation:\\nBilling is processed correctly each cycle\\n\\nEntity:\\n Product\\n id: integer\\n name: string\\n category: string\\n description: string\\n retail_price: float\\n cost_price: float\\n stock: integer\\n\\nEntity:\\n Box\\n id: integer\\n month: string\\n theme: string\\n products: list\\n retail_value: float\\n subscription_price: float\\n\\nEntity:\\n Customer\\n id: integer\\n name: string\\n email: string\\n tier: string\\n preferences: dict\\n allergies: list\\n subscription_start: datetime\\n\\nEntity:\\n Shipment\\n id: integer\\n box_id: integer\\n customer_id: integer\\n tracking_number: string\\n status: string\\n shipped_at: datetime\\n delivered_at: datetime\\n\\nBehavior:\\n CurateBox\\n Input: customer_id integer, month string, preferences dict\\n Output: box Box\\n Action: Select products matching customer preferences and tier\\n\\nBehavior:\\n AssembleBox\\n Input: box_id integer, products list\\n Output: assembly dict\\n Action: Package products into box with presentation materials\\n\\nBehavior:\\n ShipBox\\n Input: box_id integer, customer_id integer\\n Output: shipment Shipment\\n Action: Create shipment label and dispatch to carrier\\n\\nBehavior:\\n ProcessBilling\\n Input: customer_id integer, amount float\\n Output: receipt dict\\n Action: Charge subscription fee and send receipt\\n\\nCondition:\\n When: product stock falls below monthly demand forecast\\n Then: Trigger reorder from supplier and prepare substitution list\\n\\nCondition:\\n When: customer has allergy matching a product in box\\n Then: Remove product and substitute with safe alternative\\n\\nEvent:\\n On: BoxCurated\\n Do: Send preview to customer and proceed to assembly\\n\\nEvent:\\n On: BoxShipped\\n Do: Send tracking information and estimated delivery date\\n\\nEvent:\\n On: BoxDelivered\\n Do: Request feedback and product ratings\\n\\nOptimize:\\n Curation personalization\\n Priority: High\\n\\nOptimize:\\n Shipping cost efficiency\\n Priority: Medium\"}

{\"id\": \"aicl_032\", \"domain\": \"video_streaming\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a video streaming platform with content, watchlists, recommendations, and adaptive bitrate\", \"code\": \"# Video Streaming Platform\\n# Levels 1-8: Entertainment with ML recommendations\\n\\nGoal:\\nCreate a video streaming platform with content catalog, watchlist management, adaptive bitrate streaming, personalized recommendations, and subscription billing\\n\\nConstraint:\\nVideo playback must start within 3 seconds\\n\\nConstraint:\\nContent recommendations must update within 2 hours of viewing activity\\n\\nRisk:\\nContent licensing expires during active viewing\\n\\nRecovery:\\nGracefully end stream with notice and suggest similar content\\n\\nRisk:\\nServer overload during premiere events\\n\\nRecovery:\\nEnable CDN edge caching and queue new viewer connections\\n\\nRisk:\\nRecommendation algorithm creates echo chamber\\n\\nRecovery:\\nInject 20 percent diverse content outside primary preferences\\n\\nLayer:\\n Content\\n SubLayer:\\n Catalog\\n SubLayer:\\n Metadata\\n\\nLayer:\\n Playback\\n SubLayer:\\n Streaming\\n SubLayer:\\n AdaptiveBitrate\\n\\nLayer:\\n Users\\n SubLayer:\\n Profiles\\n SubLayer:\\n Watchlists\\n\\nLayer:\\n Recommendations\\n SubLayer:\\n Engine\\n SubLayer:\\n Training\\n\\nValidation:\\nVideos stream smoothly at adaptive quality\\n\\nValidation:\\nWatchlists persist across sessions\\n\\nValidation:\\nRecommendations reflect viewing history\\n\\nEntity:\\n Content\\n id: integer\\n title: string\\n type: string\\n genre: string\\n duration: integer\\n rating: float\\n release_year: integer\\n description: string\\n\\nEntity:\\n User\\n id: integer\\n username: string\\n email: string\\n subscription_tier: string\\n viewing_history: list\\n preferences: dict\\n\\nEntity:\\n WatchlistItem\\n id: integer\\n user_id: integer\\n content_id: integer\\n added_at: datetime\\n status: string\\n\\nEntity:\\n ViewingSession\\n id: integer\\n user_id: integer\\n content_id: integer\\n progress_percent: float\\n quality: string\\n started_at: datetime\\n\\nEntity:\\n Subscription\\n id: integer\\n user_id: integer\\n plan: string\\n price: float\\n status: string\\n billing_date: datetime\\n\\nBehavior:\\n StreamContent\\n Input: content_id integer, user_id integer, preferred_quality string\\n Output: stream_url string\\n Action: Authenticate and return adaptive bitrate stream URL\\n\\nBehavior:\\n AddToWatchlist\\n Input: user_id integer, content_id integer\\n Output: item WatchlistItem\\n Action: Add content to user watchlist\\n\\nBehavior:\\n RecordProgress\\n Input: session_id integer, progress_percent float, quality string\\n Output: updated_session ViewingSession\\n Action: Save viewing progress for resume capability\\n\\nBehavior:\\n GenerateRecommendations\\n Input: user_id integer, count integer\\n Output: content list\\n Action: Suggest content based on viewing history and preferences\\n\\nBehavior:\\n ProcessSubscription\\n Input: user_id integer, plan string, payment_method string\\n Output: subscription Subscription\\n Action: Create or update subscription and process payment\\n\\nCondition:\\n When: user on basic plan tries to access 4K content\\n Then: Offer upgrade to premium plan\\n\\nCondition:\\n When: viewing progress reaches 90 percent\\n Then: Mark as watched and update recommendation training data\\n\\nCondition:\\n When: network bandwidth drops below threshold\\n Then: Reduce stream quality to prevent buffering\\n\\nEvent:\\n On: ContentStarted\\n Do: Create viewing session and begin progress tracking\\n\\nEvent:\\n On: ContentCompleted\\n Do: Update history, refresh recommendations, and suggest next content\\n\\nEvent:\\n On: SubscriptionRenewed\\n Do: Extend access and send confirmation\\n\\nParallel:\\n Playback\\n Recommendations\\n\\nOptimize:\\n Stream startup latency\\n Priority: Critical\\n\\nOptimize:\\n Recommendation accuracy\\n Priority: High\\n\\nLearn:\\n content_recommendation\\n Based: viewing_history, genre_affinity, completion_rate, time_of_day, session_length\\n Adapt: weight factors based on watch-through rate and explicit ratings\"}

{\"id\": \"aicl_033\", \"domain\": \"warehouse_robotics\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a warehouse robotics system with robots, tasks, navigation, and fleet coordination\", \"code\": \"# Warehouse Robotics System\\n# Levels 1-8: Automated warehouse with ML path optimization\\n\\nGoal:\\nCreate a warehouse robotics system with robot fleet management, task assignment, navigation planning, collision avoidance, and predictive maintenance\\n\\nConstraint:\\nRobot task assignment must complete within 2 seconds\\n\\nConstraint:\\nNavigation paths must be collision-free\\n\\nRisk:\\nRobot collision in shared aisle\\n\\nRecovery:\\nEmergency stop both robots, recalculate paths, and resume with priority\\n\\nRisk:\\nRobot battery depletion during task\\n\\nRecovery:\\nRoute to nearest charging station and reassign task to another robot\\n\\nRisk:\\nNavigation sensor malfunction\\n\\nRecovery:\\nSwitch to backup sensor mode and flag for maintenance\\n\\nLayer:\\n Fleet\\n SubLayer:\\n Robots\\n SubLayer:\\n Coordination\\n\\nLayer:\\n Tasks\\n SubLayer:\\n Assignment\\n SubLayer:\\n Execution\\n\\nLayer:\\n Navigation\\n SubLayer:\\n PathPlanning\\n SubLayer:\\n CollisionAvoidance\\n\\nLayer:\\n Maintenance\\n SubLayer:\\n Monitoring\\n SubLayer:\\n Scheduling\\n\\nValidation:\\nRobots execute assigned tasks correctly\\n\\nValidation:\\nNavigation paths are collision-free\\n\\nValidation:\\nFleet is coordinated without conflicts\\n\\nEntity:\\n Robot\\n id: integer\\n name: string\\n type: string\\n battery_level: float\\n location: dict\\n status: string\\n current_task_id: integer\\n last_maintenance: datetime\\n\\nEntity:\\n Task\\n id: integer\\n type: string\\n source_location: dict\\n destination_location: dict\\n priority: integer\\n assigned_robot_id: integer\\n status: string\\n created_at: datetime\\n\\nEntity:\\n Path\\n id: integer\\n robot_id: integer\\n waypoints: list\\n estimated_duration: integer\\n collision_risk: float\\n\\nEntity:\\n Zone\\n id: integer\\n name: string\\n type: string\\n capacity: integer\\n current_occupancy: integer\\n restricted: boolean\\n\\nEntity:\\n MaintenanceLog\\n id: integer\\n robot_id: integer\\n type: string\\n description: string\\n performed_at: datetime\\n next_due_at: datetime\\n\\nBehavior:\\n AssignTask\\n Input: task Task, available_robots list\\n Output: assigned_robot Robot\\n Action: Find best available robot based on location, battery, and capacity\\n\\nBehavior:\\n PlanPath\\n Input: robot_id integer, source dict, destination dict\\n Output: path Path\\n Action: Calculate optimal path avoiding obstacles and other robots\\n\\nBehavior:\\n ExecuteTask\\n Input: robot_id integer, task_id integer\\n Output: result dict\\n Action: Navigate robot through path and perform task\\n\\nBehavior:\\n MonitorBattery\\n Input: robot_id integer\\n Output: status dict\\n Action: Check battery level and schedule charging if needed\\n\\nBehavior:\\n CoordinateFleet\\n Input: active_robots list, pending_tasks list\\n Output: assignments dict\\n Action: Optimize task distribution across fleet\\n\\nBehavior:\\n DetectCollision\\n Input: paths list\\n Output: conflicts list\\n Action: Identify potential collisions between planned paths\\n\\nCondition:\\n When: robot battery falls below 20 percent\\n Then: Route to charging station and reassign current task\\n\\nCondition:\\n When: two paths intersect at same time\\n Then: Apply priority-based right-of-way and recalculate lower priority path\\n\\nCondition:\\n When: zone occupancy reaches capacity\\n Then: Queue robots at zone entry and redirect to alternative zones\\n\\nEvent:\\n On: TaskCompleted\\n Do: Update robot availability and check pending task queue\\n\\nEvent:\\n On: CollisionDetected\\n Do: Emergency stop affected robots and recalculate paths\\n\\nEvent:\\n On: BatteryLow\\n Do: Route robot to charging station and reassign task\\n\\nParallel:\\n Fleet\\n Navigation\\n\\nOptimize:\\n Path planning speed\\n Priority: Critical\\n\\nOptimize:\\n Fleet utilization rate\\n Priority: High\\n\\nLearn:\\n path_optimization\\n Based: warehouse_layout, traffic_patterns, time_of_day, task_frequency\\n Adapt: pre-compute popular paths and adjust timing based on congestion patterns\"}

{\"id\": \"aicl_034\", \"domain\": \"insurance_claims\", \"levels\": [1,2,3,4,5,7,9], \"description\": \"Build an insurance claims processing system with policies, claims, assessments, and payouts\", \"code\": \"# Insurance Claims Processing\\n# Levels 1-5, 7, 9: Claims with security and optimization\\n\\nGoal:\\nCreate an insurance claims processing system with policy management, claim submission, damage assessment, payout processing, and fraud detection\\n\\nConstraint:\\nClaims must be acknowledged within 24 hours\\n\\nConstraint:\\nPayout for approved claims must be processed within 5 business days\\n\\nRisk:\\nFraudulent claim submitted\\n\\nRecovery:\\nFlag for investigation and suspend payout pending review\\n\\nRisk:\\nClaim assessment delay beyond policy SLA\\n\\nRecovery:\\nEscalate to senior assessor and notify policyholder of extended timeline\\n\\nRisk:\\nPolicyholder data breach\\n\\nRecovery:\\nLock account, notify affected parties, and comply with breach disclosure laws\\n\\nLayer:\\n Policies\\n SubLayer:\\n Management\\n SubLayer:\\n Coverage\\n\\nLayer:\\n Claims\\n SubLayer:\\n Submission\\n SubLayer:\\n Assessment\\n\\nLayer:\\n Payouts\\n SubLayer:\\n Processing\\n SubLayer:\\n Fraud\\n\\nValidation:\\nClaims are processed within SLA\\n\\nValidation:\\nFraud detection identifies suspicious claims\\n\\nEntity:\\n Policy\\n id: integer\\n holder_id: integer\\n type: string\\n coverage_amount: float\\n premium: float\\n deductible: float\\n start_date: datetime\\n end_date: datetime\\n status: string\\n\\nEntity:\\n Claim\\n id: integer\\n policy_id: integer\\n type: string\\n description: string\\n amount: float\\n evidence: list\\n status: string\\n submitted_at: datetime\\n\\nEntity:\\n Assessment\\n id: integer\\n claim_id: integer\\n assessor_id: integer\\n approved_amount: float\\n notes: string\\n completed_at: datetime\\n\\nEntity:\\n Payout\\n id: integer\\n claim_id: integer\\n amount: float\\n method: string\\n status: string\\n processed_at: datetime\\n\\nEntity:\\n PolicyHolder\\n id: integer\\n name: string\\n email: string\\n phone: string\\n policies: list\\n claim_history: list\\n\\nBehavior:\\n SubmitClaim\\n Input: policy_id integer, type string, description string, amount float, evidence list\\n Output: claim Claim\\n Action: Create claim and acknowledge receipt to policyholder\\n\\nBehavior:\\n AssessClaim\\n Input: claim_id integer, assessor_id integer\\n Output: assessment Assessment\\n Action: Review evidence and determine approved payout amount\\n\\nBehavior:\\n ProcessPayout\\n Input: claim_id integer, amount float, method string\\n Output: payout Payout\\n Action: Execute payout and send confirmation to policyholder\\n\\nBehavior:\\n DetectFraud\\n Input: claim Claim, history list\\n Output: risk_score float\\n Action: Analyze claim against fraud indicators and historical patterns\\n\\nCondition:\\n When: claim amount exceeds policy coverage limit\\n Then: Cap payout at coverage limit and notify policyholder\\n\\nCondition:\\n When: fraud risk score exceeds 70 percent\\n Then: Flag for investigation and suspend automatic payout\\n\\nEvent:\\n On: ClaimSubmitted\\n Do: Acknowledge receipt and assign to assessor\\n\\nEvent:\\n On: ClaimApproved\\n Do: Initiate payout process and notify policyholder\\n\\nOptimize:\\n Claims processing speed\\n Priority: High\\n\\nEncrypt:\\n policy holder personal data\\n claim evidence documents\\n\\nProtect:\\n Policyholder data from unauthorized access\\n Claims from fraudulent modification\"}

{\"id\": \"aicl_035\", \"domain\": \"meal_prep\", \"levels\": [1,2,3,4,5,8], \"description\": \"Build a meal prep and recipe platform with ingredients, recipes, meal plans, and nutritional tracking\", \"code\": \"# Meal Prep and Recipe Platform\\n# Levels 1-5, 8: Cooking with adaptive learning\\n\\nGoal:\\nCreate a meal prep and recipe platform with recipe management, ingredient tracking, automated meal planning, grocery list generation, and adaptive nutritional optimization\\n\\nConstraint:\\nMeal plans must meet daily nutritional targets within 10 percent\\n\\nConstraint:\\nGrocery lists must account for ingredient overlap across recipes\\n\\nRisk:\\nRecipe contains undeclared allergen\\n\\nRecovery:\\nFlag recipe for review and alert users who saved it\\n\\nRisk:\\nNutritional calculation error\\n\\nRecovery:\\nRecalculate from ingredient database and update all affected meal plans\\n\\nLayer:\\n Recipes\\n SubLayer:\\n Management\\n SubLayer:\\n Search\\n\\nLayer:\\n Planning\\n SubLayer:\\n MealPlans\\n SubLayer:\\n GroceryLists\\n\\nLayer:\\n Nutrition\\n SubLayer:\\n Tracking\\n SubLayer:\\n Optimization\\n\\nValidation:\\nRecipes have complete ingredient lists and instructions\\n\\nValidation:\\nMeal plans meet nutritional targets\\n\\nValidation:\\nGrocery lists are accurate and deduplicated\\n\\nEntity:\\n Recipe\\n id: integer\\n title: string\\n servings: integer\\n prep_time: integer\\n cook_time: integer\\n ingredients: list\\n instructions: list\\n calories: integer\\n macros: dict\\n tags: list\\n\\nEntity:\\n Ingredient\\n id: integer\\n name: string\\n category: string\\n unit: string\\n calories_per_unit: float\\n macros_per_unit: dict\\n allergens: list\\n\\nEntity:\\n MealPlan\\n id: integer\\n user_id: integer\\n week: string\\n days: dict\\n total_calories: integer\\n total_macros: dict\\n\\nEntity:\\n GroceryList\\n id: integer\\n meal_plan_id: integer\\n items: list\\n estimated_cost: float\\n\\nEntity:\\n User\\n id: integer\\n name: string\\n daily_calorie_target: integer\\n macro_targets: dict\\n allergies: list\\n preferences: list\\n\\nBehavior:\\n CreateRecipe\\n Input: title string, ingredients list, instructions list, servings integer\\n Output: recipe Recipe\\n Action: Create recipe with auto-calculated nutrition from ingredients\\n\\nBehavior:\\n GenerateMealPlan\\n Input: user_id integer, week string, preferences list, target_calories integer\\n Output: meal_plan MealPlan\\n Action: Select recipes meeting nutritional targets and preferences\\n\\nBehavior:\\n GenerateGroceryList\\n Input: meal_plan_id integer\\n Output: grocery_list GroceryList\\n Action: Aggregate and deduplicate ingredients across all recipes\\n\\nBehavior:\\n CalculateNutrition\\n Input: ingredients list, servings integer\\n Output: nutrition dict\\n Action: Sum calories and macros from ingredient database\\n\\nBehavior:\\n SubstituteIngredient\\n Input: recipe_id integer, old_ingredient string, new_ingredient string\\n Output: updated_recipe Recipe\\n Action: Replace ingredient and recalculate nutrition\\n\\nCondition:\\n When: recipe contains allergen matching user allergy list\\n Then: Exclude recipe from meal plan and suggest safe alternative\\n\\nCondition:\\n When: meal plan daily calories deviate more than 10 percent from target\\n Then: Flag for rebalancing and suggest portion adjustments\\n\\nEvent:\\n On: RecipeCreated\\n Do: Index for search and calculate nutrition\\n\\nEvent:\\n On: MealPlanGenerated\\n Do: Generate grocery list and send to user\\n\\nLearn:\\n taste_preference\\n Based: recipe_ratings, meal_plan_selections, cuisine_frequency, cooking_history\\n Adapt: weight recipe recommendations based on learned taste profile\\n\\nLearn:\\n budget_optimization\\n Based: grocery_prices, seasonal_availability, purchase_frequency\\n Adapt: suggest cost-effective ingredient substitutions\"}

{\"id\": \"aicl_036\", \"domain\": \"container_shipping\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build a container shipping management system with containers, vessels, ports, and cargo tracking\", \"code\": \"# Container Shipping Management\\n# Levels 1-7: Maritime logistics platform\\n\\nGoal:\\nCreate a container shipping management system with vessel scheduling, container tracking, port operations, cargo management, and route optimization\\n\\nConstraint:\\nContainer tracking must update every 30 minutes\\n\\nConstraint:\\nPort berth assignments must be conflict-free\\n\\nRisk:\\nContainer lost or damaged at sea\\n\\nRecovery:\\nFile insurance claim, notify cargo owner, and initiate recovery procedure\\n\\nRisk:\\nPort congestion causes vessel delays\\n\\nRecovery:\\nReroute to alternative port and adjust downstream schedule\\n\\nRisk:\\nHazardous cargo incident\\n\\nRecovery:\\nActivate emergency protocol and notify port authority\\n\\nLayer:\\n Vessels\\n SubLayer:\\n Fleet\\n SubLayer:\\n Schedule\\n\\nLayer:\\n Containers\\n SubLayer:\\n Tracking\\n SubLayer:\\n Allocation\\n\\nLayer:\\n Ports\\n SubLayer:\\n Operations\\n SubLayer:\\n Berths\\n\\nLayer:\\n Cargo\\n SubLayer:\\n Booking\\n SubLayer:\\n Documentation\\n\\nValidation:\\nContainers are tracked from loading to unloading\\n\\nValidation:\\nPort operations are scheduled without conflicts\\n\\nValidation:\\nCargo documentation is complete for customs\\n\\nEntity:\\n Vessel\\n id: integer\\n name: string\\n capacity_teu: integer\\n current_voyage_id: integer\\n status: string\\n speed_knots: float\\n flag: string\\n\\nEntity:\\n Container\\n id: integer\\n number: string\\n size: string\\n type: string\\n vessel_id: integer\\n port_id: integer\\n status: string\\n cargo_type: string\\n weight_kg: float\\n\\nEntity:\\n Port\\n id: integer\\n name: string\\n country: string\\n berth_count: integer\\n crane_count: integer\\n capacity_teu: integer\\n\\nEntity:\\n Voyage\\n id: integer\\n vessel_id: integer\\n origin_port_id: integer\\n destination_port_id: integer\\n departure: datetime\\n eta: datetime\\n status: string\\n containers: list\\n\\nEntity:\\n CargoBooking\\n id: integer\\n container_id: integer\\n shipper_id: integer\\n commodity: string\\n weight_kg: float\\n value: float\\n hazardous: boolean\\n status: string\\n\\nBehavior:\\n ScheduleVoyage\\n Input: vessel_id integer, origin_port_id integer, destination_port_id integer, departure datetime\\n Output: voyage Voyage\\n Action: Create voyage and assign available containers\\n\\nBehavior:\\n TrackContainer\\n Input: container_id integer\\n Output: location dict\\n Action: Retrieve current location and status of container\\n\\nBehavior:\\n AssignBerth\\n Input: vessel_id integer, port_id integer, arrival datetime\\n Output: berth dict\\n Action: Find available berth and create port call schedule\\n\\nBehavior:\\n BookCargo\\n Input: shipper_id integer, commodity string, weight float, hazardous boolean\\n Output: booking CargoBooking\\n Action: Reserve container space and generate shipping documents\\n\\nBehavior:\\n OptimizeRoute\\n Input: origin_port_id integer, destination_port_id integer, weather dict\\n Output: route dict\\n Action: Calculate optimal route considering weather and fuel efficiency\\n\\nCondition:\\n When: vessel ETA delayed by more than 24 hours\\n Then: Notify all cargo owners and adjust downstream port schedules\\n\\nCondition:\\n When: hazardous cargo requires special handling\\n Then: Verify compliance documentation and assign designated berth\\n\\nEvent:\\n On: VesselArrived\\n Do: Initiate berth assignment and container unloading plan\\n\\nEvent:\\n On: ContainerUnloaded\\n Do: Update tracking and notify cargo owner of availability\\n\\nEvent:\\n On: VoyageCompleted\\n Do: Close voyage record and update vessel maintenance schedule\\n\\nParallel:\\n Vessels\\n Containers\\n\\nOptimize:\\n Route fuel efficiency\\n Priority: High\\n\\nOptimize:\\n Port turnaround time\\n Priority: High\"}

{\"id\": \"aicl_037\", \"domain\": \"online_auction\", \"levels\": [1,2,3,4,5,6,7], \"description\": \"Build an online auction platform with listings, bids, timers, and escrow payments\", \"code\": \"# Online Auction Platform\\n# Levels 1-7: Bidding with concurrency and pricing\\n\\nGoal:\\nCreate an online auction platform with item listings, real-time bidding, auction timers, escrow payments, and dispute resolution\\n\\nConstraint:\\nBid processing must complete within 500 milliseconds\\n\\nConstraint:\\nAuction timer must be accurate to the second\\n\\nRisk:\\nSnipe bidding in last seconds\\n\\nRecovery:\\nExtend auction by 5 minutes when bid placed in final 30 seconds\\n\\nRisk:\\nWinning bidder defaults on payment\\n\\nRecovery:\\nOffer item to second-highest bidder and penalize defaulting bidder\\n\\nRisk:\\nConcurrent bid creates inconsistency\\n\\nRecovery:\\nResolve by timestamp priority and notify affected bidders\\n\\nLayer:\\n Listings\\n SubLayer:\\n Items\\n SubLayer:\\n Categories\\n\\nLayer:\\n Bidding\\n SubLayer:\\n Auctions\\n SubLayer:\\n Bids\\n\\nLayer:\\n Payments\\n SubLayer:\\n Escrow\\n SubLayer:\\n Settlement\\n\\nValidation:\\nBids are processed in correct time order\\n\\nValidation:\\nWinning bidder is correctly determined\\n\\nValidation:\\nEscrow payments are held and released properly\\n\\nEntity:\\n AuctionItem\\n id: integer\\n title: string\\n description: string\\n starting_price: float\\n current_price: float\\n seller_id: integer\\n category: string\\n condition: string\\n\\nEntity:\\n Auction\\n id: integer\\n item_id: integer\\n start_time: datetime\\n end_time: datetime\\n reserve_price: float\\n status: string\\n bid_count: integer\\n\\nEntity:\\n Bid\\n id: integer\\n auction_id: integer\\n bidder_id: integer\\n amount: float\\n timestamp: datetime\\n is_winning: boolean\\n\\nEntity:\\n Escrow\\n id: integer\\n auction_id: integer\\n buyer_id: integer\\n amount: float\\n status: string\\n created_at: datetime\\n\\nEntity:\\n User\\n id: integer\\n username: string\\n email: string\\n rating: float\\n auctions_won: integer\\n\\nBehavior:\\n ListAuctionItem\\n Input: seller_id integer, title string, description string, starting_price float, duration_hours integer\\n Output: auction Auction\\n Action: Create auction listing with timer and reserve price\\n\\nBehavior:\\n PlaceBid\\n Input: auction_id integer, bidder_id integer, amount float\\n Output: bid Bid\\n Action: Validate bid amount and record with timestamp\\n\\nBehavior:\\n CloseAuction\\n Input: auction_id integer\\n Output: result dict\\n Action: Determine winner and initiate escrow payment\\n\\nBehavior:\\n ProcessEscrow\\n Input: auction_id integer, buyer_id integer, amount float\\n Output: escrow Escrow\\n Action: Hold payment in escrow until item delivery confirmed\\n\\nBehavior:\\n ReleaseEscrow\\n Input: escrow_id integer, confirmation dict\\n Output: settlement dict\\n Action: Release funds to seller after delivery confirmation\\n\\nCondition:\\n When: bid placed within last 30 seconds of auction\\n Then: Extend auction end time by 5 minutes\\n\\nCondition:\\n When: winning bid is below reserve price\\n Then: Notify seller and allow them to accept or reject\\n\\nCondition:\\n When: bidder has unpaid items from previous auctions\\n Then: Block new bids until outstanding payments are resolved\\n\\nEvent:\\n On: BidPlaced\\n Do: Update current price, notify previous highest bidder, and check auto-extend\\n\\nEvent:\\n On: AuctionClosed\\n Do: Determine winner and initiate escrow process\\n\\nEvent:\\n On: DeliveryConfirmed\\n Do: Release escrow funds to seller\\n\\nParallel:\\n Bidding\\n Payments\\n\\nOptimize:\\n Bid processing latency\\n Priority: Critical\\n\\nOptimize:\\n Auction timer accuracy\\n Priority: Critical\"}

{\"id\": \"aicl_038\", \"domain\": \"calculator\", \"levels\": [1,2,3], \"description\": \"Build a simple calculator application\", \"code\": \"# Simple Calculator\\n# Levels 1-3: Basic arithmetic\\n\\nGoal:\\nCreate a calculator that performs basic arithmetic operations\\n\\nRisk:\\nDivision by zero\\n\\nRecovery:\\nReturn error message and prevent calculation\\n\\nRisk:\\nInput overflow beyond maximum value\\n\\nRecovery:\\nClamp result to maximum and warn user\\n\\nLayer:\\n Calculation\\n\\nValidation:\\nAll arithmetic operations produce correct results\\n\\nEntity:\\n Operation\\n type: string\\n operand_a: float\\n operand_b: float\\n result: float\\n\\nBehavior:\\n Add\\n Input: a float, b float\\n Output: result float\\n Action: Return sum of a and b\\n\\nBehavior:\\n Subtract\\n Input: a float, b float\\n Output: result float\\n Action: Return difference of a and b\\n\\nBehavior:\\n Multiply\\n Input: a float, b float\\n Output: result float\\n Action: Return product of a and b\\n\\nBehavior:\\n Divide\\n Input: a float, b float\\n Output: result float\\n Action: Return quotient of a divided by b\"}

{\"id\": \"aicl_039\", \"domain\": \"todo_app\", \"levels\": [1,2,3,4], \"description\": \"Build a todo list application\", \"code\": \"# Todo List Application\\n# Levels 1-4: Task management\\n\\nGoal:\\nCreate a todo list application with task creation, completion, and priority management\\n\\nRisk:\\nTask deleted accidentally\\n\\nRecovery:\\nMove to trash and allow recovery for 30 days\\n\\nLayer:\\n Tasks\\n SubLayer:\\n Active\\n SubLayer:\\n Completed\\n\\nValidation:\\nTasks can be created, completed, and deleted\\n\\nEntity:\\n Task\\n id: integer\\n title: string\\n description: string\\n priority: string\\n due_date: datetime\\n completed: boolean\\n created_at: datetime\\n\\nEntity:\\n Category\\n id: integer\\n name: string\\n color: string\\n task_count: integer\\n\\nBehavior:\\n CreateTask\\n Input: title string, description string, priority string, due_date datetime\\n Output: task Task\\n Action: Create new task with specified attributes\\n\\nBehavior:\\n CompleteTask\\n Input: task_id integer\\n Output: updated_task Task\\n Action: Mark task as completed and move to completed list\\n\\nBehavior:\\n DeleteTask\\n Input: task_id integer\\n Output: result dict\\n Action: Move task to trash for recovery\\n\\nCondition:\\n When: task due date has passed and task is not completed\\n Then: Flag task as overdue and highlight in list\"}

{\"id\": \"aicl_040\", \"domain\": \"temperature_converter\", \"levels\": [1,2,3], \"description\": \"Build a temperature converter between Celsius, Fahrenheit, and Kelvin\", \"code\": \"# Temperature Converter\\n# Levels 1-3: Simple unit conversion\\n\\nGoal:\\nCreate a temperature converter supporting Celsius, Fahrenheit, and Kelvin\\n\\nRisk:\\nTemperature below absolute zero\\n\\nRecovery:\\nClamp to absolute zero and warn user\\n\\nLayer:\\n Conversion\\n\\nValidation:\\nConversions between all three scales are accurate\\n\\nEntity:\\n Temperature\\n value: float\\n unit: string\\n\\nBehavior:\\n CelsiusToFahrenheit\\n Input: celsius float\\n Output: fahrenheit float\\n Action: Convert celsius to fahrenheit using formula\\n\\nBehavior:\\n FahrenheitToCelsius\\n Input: fahrenheit float\\n Output: celsius float\\n Action: Convert fahrenheit to celsius using formula\\n\\nBehavior:\\n CelsiusToKelvin\\n Input: celsius float\\n Output: kelvin float\\n Action: Convert celsius to kelvin by adding 273.15\"}

{\"id\": \"aicl_041\", \"domain\": \"note_taking\", \"levels\": [1,2,3,4,5], \"description\": \"Build a note-taking application with folders, tags, search, and sharing\", \"code\": \"# Note Taking Application\\n# Levels 1-5: Notes with events\\n\\nGoal:\\nCreate a note-taking application with folders, tags, full-text search, version history, and note sharing\\n\\nConstraint:\\nSearch must return results within 500 milliseconds\\n\\nRisk:\\nNote accidentally deleted\\n\\nRecovery:\\nMove to trash and retain for 60 days with version history\\n\\nRisk:\\nSync conflict between devices\\n\\nRecovery:\\nCreate conflict copy and prompt user to merge\\n\\nLayer:\\n Notes\\n SubLayer:\\n Content\\n SubLayer:\\n Versions\\n\\nLayer:\\n Organization\\n SubLayer:\\n Folders\\n SubLayer:\\n Tags\\n\\nValidation:\\nNotes can be created, edited, and searched\\n\\nValidation:\\nVersion history is maintained accurately\\n\\nEntity:\\n Note\\n id: integer\\n title: string\\n content: string\\n folder_id: integer\\n tags: list\\n created_at: datetime\\n updated_at: datetime\\n version: integer\\n\\nEntity:\\n Folder\\n id: integer\\n name: string\\n parent_id: integer\\n note_count: integer\\n\\nEntity:\\n Tag\\n id: integer\\n name: string\\n color: string\\n note_count: integer\\n\\nEntity:\\n ShareLink\\n id: integer\\n note_id: integer\\n token: string\\n expires_at: datetime\\n access_count: integer\\n\\nBehavior:\\n CreateNote\\n Input: title string, content string, folder_id integer, tags list\\n Output: note Note\\n Action: Create note in specified folder with tags\\n\\nBehavior:\\n SearchNotes\\n Input: query string, tags list, folder_id integer\\n Output: notes list\\n Action: Full-text search with tag and folder filters\\n\\nBehavior:\\n ShareNote\\n Input: note_id integer, expires_at datetime\\n Output: link ShareLink\\n Action: Generate shareable link with expiration\\n\\nCondition:\\n When: note content exceeds 1 million characters\\n Then: Warn about performance and suggest splitting into multiple notes\\n\\nEvent:\\n On: NoteUpdated\\n Do: Save version snapshot and update search index\\n\\nEvent:\\n On: NoteShared\\n Do: Track access count and notify owner on first view\"}

{\"id\": \"aicl_042\", \"domain\": \"password_manager\", \"levels\": [1,2,3,4,9], \"description\": \"Build a password manager with vaults, entries, encryption, and auto-fill\", \"code\": \"# Password Manager\\n# Levels 1-4, 9: Security-focused credential storage\\n\\nGoal:\\nCreate a password manager with encrypted vaults, credential entries, password generation, auto-fill, and breach monitoring\\n\\nConstraint:\\nAll credentials must be encrypted with AES-256\\n\\nConstraint:\\nMaster password must never be stored in plain text\\n\\nRisk:\\nMaster password compromised\\n\\nRecovery:\\nForce credential re-encryption and notify user of security event\\n\\nRisk:\\nCredential breach detected in haveibeenpwned\\n\\nRecovery:\\nAlert user and prompt immediate password change for affected accounts\\n\\nLayer:\\n Vault\\n SubLayer:\\n Entries\\n SubLayer:\\n Generation\\n\\nLayer:\\n Security\\n SubLayer:\\n Encryption\\n SubLayer:\\n BreachMonitor\\n\\nValidation:\\nCredentials are stored encrypted and decrypted only on demand\\n\\nValidation:\\nPassword generator creates strong passwords\\n\\nEntity:\\n Vault\\n id: integer\\n user_id: integer\\n name: string\\n entries_count: integer\\n created_at: datetime\\n\\nEntity:\\n Credential\\n id: integer\\n vault_id: integer\\n service: string\\n username: string\\n password_encrypted: string\\n url: string\\n notes: string\\n updated_at: datetime\\n\\nEntity:\\n User\\n id: integer\\n email: string\\n master_password_hash: string\\n mfa_enabled: boolean\\n\\nBehavior:\\n AddCredential\\n Input: vault_id integer, service string, username string, password string, url string\\n Output: credential Credential\\n Action: Encrypt and store credential in vault\\n\\nBehavior:\\n RetrieveCredential\\n Input: credential_id integer, master_password string\\n Output: decrypted dict\\n Action: Decrypt and return credential for auto-fill\\n\\nBehavior:\\n GeneratePassword\\n Input: length integer, include_symbols boolean, include_numbers boolean\\n Output: password string\\n Action: Generate cryptographically random password\\n\\nBehavior:\\n CheckBreach\\n Input: credential_id integer\\n Output: breached boolean\\n Action: Check credential against known breach databases\\n\\nCondition:\\n When: password is found in breach database\\n Then: Alert user immediately and prompt password change\\n\\nCondition:\\n When: vault is unlocked for more than 30 minutes\\n Then: Auto-lock vault and require re-authentication\\n\\nEvent:\\n On: CredentialAdded\\n Do: Update vault index and check against breach database\\n\\nEvent:\\n On: BreachDetected\\n Do: Alert user and suggest password rotation for affected service\\n\\nEncrypt:\\n credential password_encrypted\\n credential notes\\n\\nProtect:\\n Vault from unauthorized access\\n Master password from extraction\\n Credentials from interception\"}

{\"id\": \"aicl_043\", \"domain\": \"blogging_platform\", \"levels\": [1,2,3,4,5,6,7,8], \"description\": \"Build a blogging platform with posts, authors, comments, SEO, and content recommendations\", \"code\": \"# Blogging Platform\\n# Levels 1-8: Content publishing with ML\\n\\nGoal:\\nCreate a blogging platform with post management, rich editing, SEO optimization, comment moderation, and content recommendations\\n\\nConstraint:\\nPage load time must be under 2 seconds\\n\\nConstraint:\\nSEO score must be calculated for every post\\n\\nRisk:\\nSpam comments flood blog\\n\\nRecovery:\\nAuto-moderate spam and enable CAPTCHA for flagged users\\n\\nRisk:\\nPost content loss during editing\\n\\nRecovery:\\nAuto-save draft every 30 seconds and restore from version history\\n\\nRisk:\\nSEO penalty from duplicate content\\n\\nRecovery:\\nDetect duplicates and canonicalize URLs\\n\\nLayer:\\n Content\\n SubLayer:\\n Posts\\n SubLayer:\\n Drafts\\n\\nLayer:\\n Engagement\\n SubLayer:\\n Comments\\n SubLayer:\\n Reactions\\n\\nLayer:\\n SEO\\n SubLayer:\\n Analysis\\n SubLayer:\\n Optimization\\n\\nLayer:\\n Discovery\\n SubLayer:\\n Recommendations\\n SubLayer:\\n RelatedPosts\\n\\nValidation:\\nPosts can be published, edited, and scheduled\\n\\nValidation:\\nSEO analysis provides actionable recommendations\\n\\nValidation:\\nComments are moderated effectively\\n\\nEntity:\\n Post\\n id: integer\\n title: string\\n slug: string\\n content: string\\n author_id: integer\\n status: string\\n seo_score: float\\n published_at: datetime\\n view_count: integer\\n tags: list\\n\\nEntity:\\n Author\\n id: integer\\n name: string\\n bio: string\\n avatar_url: string\\n post_count: integer\\n follower_count: integer\\n\\nEntity:\\n Comment\\n id: integer\\n post_id: integer\\n author_name: string\\n content: string\\n status: string\\n created_at: datetime\\n\\nEntity:\\n SEOAnalysis\\n id: integer\\n post_id: integer\\n score: float\\n issues: list\\n suggestions: list\\n keyword_density: dict\\n\\nBehavior:\\n CreatePost\\n Input: title string, content string, author_id integer, tags list\\n Output: post Post\\n Action: Create draft post with auto-generated slug\\n\\nBehavior:\\n PublishPost\\n Input: post_id integer, scheduled_at datetime\\n Output: published_post Post\\n Action: Run SEO analysis and publish or schedule post\\n\\nBehavior:\\n AnalyzeSEO\\n Input: post_id integer\\n Output: analysis SEOAnalysis\\n Action: Calculate SEO score and generate improvement suggestions\\n\\nBehavior:\\n ModerateComment\\n Input: comment_id integer, action string\\n Output: result dict\\n Action: Approve, reject, or flag comment\\n\\nBehavior:\\n GetRecommendations\\n Input: post_id integer, count integer\\n Output: posts list\\n Action: Suggest related posts based on content similarity\\n\\nCondition:\\n When: post SEO score falls below 50\\n Then: Block publication and show required improvements\\n\\nCondition:\\n When: comment matches spam pattern\\n Then: Auto-quarantine and require manual review\\n\\nEvent:\\n On: PostPublished\\n Do: Update sitemap, notify subscribers, and index for search\\n\\nEvent:\\n On: CommentSubmitted\\n Do: Run spam check and notify post author\\n\\nParallel:\\n Content\\n SEO\\n\\nOptimize:\\n Page load speed\\n Priority: Critical\\n\\nOptimize:\\n SEO analysis speed\\n Priority: High\\n\\nLearn:\\n content_recommendation\\n Based: reading_history, topic_similarity, engagement_metrics, author_affinity\\n Adapt: weight recommendation factors based on click-through and time-on-page\"}

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