task_id stringlengths 17 84 | title stringlengths 5 62 ⌀ | summary stringlengths 76 379 | category stringclasses 14
values | subdomain stringlengths 6 53 | task_split stringclasses 3
values | task_prompt stringlengths 502 5.84k | agent_must_do listlengths 0 9 | software listlengths 0 9 | input_files listlengths 0 14 | taxonomy dict | source_repo_path stringlengths 23 90 |
|---|---|---|---|---|---|---|---|---|---|---|---|
agriculture_env/crop_rotation_d02 | Crop Rotation Audit for Stable Parcels | Audit French stable agricultural parcels for crop-rotation eligibility and risk, then produce full eligible and flagged GeoPackage outputs with exact schema and layer requirements. | agriculture_env | Precision Agriculture | near-term | You are auditing crop rotation eligibility for stable agricultural parcels in France.
Task directory:
- `base`
Visible inputs:
- Parcel GeoPackage: `base\input\seq1524_d02.gpkg`
- Task brief: `base\input\task_prompt.md`
- Citation note: `base\input\citation.txt`
- Optional Python runtime manifest:
- `base\input\run... | [
"Read the staged crop-rotation brief in `input/task_prompt.md` and apply it exactly.",
"Load the `seq1524_d02` layer from `input/seq1524_d02.gpkg`.",
"Compute the eight required audit columns for every eligible `id_lcp`.",
"Write `output/eligible_units.gpkg` with preserved original columns and geometry plus t... | [
"Python",
"GeoPandas",
"pandas",
"pyogrio",
"shapely",
"pyproj",
"fiona"
] | [
{
"name": "seq1524_d02.gpkg",
"format": "GeoPackage",
"path": "input/seq1524_d02.gpkg",
"description": "Stable agricultural parcel polygons with yearly crop-code columns from 2015 through 2024"
},
{
"name": "task_prompt.md",
"format": "Markdown",
"path": "input/task_prompt.md",
"... | {
"domain_id": "12",
"domain_code": "agriculture_env",
"subdomain_id": "12.2",
"subdomain_code": "precision_ag",
"subdomain_name": "Precision Agriculture"
} | tasks/agriculture_env/crop_rotation_d02 |
agriculture_env/ndvi_zonal_statistics_d02 | Sentinel-2 NDVI Zonal Statistics | Compute a Sentinel-2 NDVI GeoTIFF and polygon-level NDVI zonal statistics for agricultural units with a CRS mismatch between vector and raster inputs. | agriculture_env | Precision Agriculture | full-spectrum | You are given a polygon shapefile of agricultural units and a 6-band Sentinel-2 GeoTIFF.
Task directory:
`base`
Inputs:
- `base\input\rotation_count.shp`
- associated `.dbf`, `.shx`, `.prj`, and `.cpg` files are present in the same directory
- each feature has `id_lcp`, `rot_count`, and polygon geometry
- `base\i... | [
"Use the staged shapefile `input/rotation_count.shp` and Sentinel-2 raster `input/s2_l2a_30m_clip.tif`.",
"Compute NDVI from raster band 4 and band 3 without extra scaling or masking.",
"Write `output/ndvi.tif` as single-band float32 with the same georeferencing as the input raster.",
"Reproject polygons to t... | [
"Python",
"rasterio",
"geopandas"
] | [
{
"name": "rotation_count.shp",
"format": "ESRI Shapefile",
"path": "input/rotation_count.shp",
"description": "Agricultural unit polygons with `id_lcp` and precomputed `rot_count` fields"
},
{
"name": "s2_l2a_30m_clip.tif",
"format": "GeoTIFF",
"path": "input/s2_l2a_30m_clip.tif",
... | {
"domain_id": "12",
"domain_code": "agriculture_env",
"subdomain_id": "12.2",
"subdomain_code": "precision_ag",
"subdomain_name": "Precision Agriculture"
} | tasks/agriculture_env/ndvi_zonal_statistics_d02 |
business_finance/american_option_pricing_ls | American Option Pricing via Longstaff-Schwartz | Implement a three-tier Monte Carlo option-pricing benchmark covering Black-Scholes validation, a single-asset American put solved with Longstaff-Schwartz regression, and a correlated 5-asset basket American put with pathwise Greeks. | business_finance | Quantitative Finance & Trading | full-spectrum | You are working on an Ubuntu coding task about pricing American-style options with Monte Carlo and Longstaff-Schwartz regression.
Task directory:
- `base`
Visible input files:
- Problem specification: `base/input/problem_spec.md`
- Python runtime manifest: `base/input/runtime_env/pyproject.toml`
- Python lockfile: `b... | [
"Read the staged problem specification in full before implementing the solver.",
"Use the staged Python wrapper if a task-local NumPy/SciPy runtime is needed.",
"Produce `results.json` and `exercise_boundary_tier2.npy` under the writable output directory.",
"Keep the reported metrics, exercise boundary sample... | [
"Python",
"NumPy",
"SciPy",
"uv"
] | [
{
"name": "problem_spec.md",
"format": "Markdown",
"path": "input/problem_spec.md",
"description": "Full public specification for the three-tier American option pricing benchmark."
},
{
"name": "runtime_env/pyproject.toml",
"format": "TOML",
"path": "input/runtime_env/pyproject.toml"... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.7",
"subdomain_code": "quant_finance",
"subdomain_name": "Quantitative Finance & Trading"
} | tasks/business_finance/american_option_pricing_ls |
business_finance/ar_full_1500 | Annual Report Full Extraction (1500 files) | Download 1500 annual reports from East Money, extract core technical personnel records, and produce one consolidated workbook with the required schema. | business_finance | Accounting & Finance | last-exam | Task Type:
- Full pipeline (download + parse + structure output), with file verification.
Goal:
- Download all reports listed in base\input\file_list.txt from East Money (东方财富).
- Extract core technical personnel data.
- Output one consolidated Excel file.
Input Files:
1) base\input\file_list.txt
- UTF-8 text file... | [
"Read `input/file_list.txt` to determine the 1500 annual-report PDFs in scope",
"Download all listed annual reports from East Money into `output/downloads/`",
"Extract core technical personnel data for each report",
"Produce `final_dataset.xlsx` with the required columns and data rules"
] | [
"Microsoft Edge",
"Python"
] | [
{
"name": "file_list.txt",
"format": "txt",
"path": "input/file_list.txt",
"description": "UTF-8 list of 1500 annual-report PDF filenames"
}
] | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.1",
"subdomain_code": "accounting",
"subdomain_name": "Accounting & Finance"
} | tasks/business_finance/ar_full_1500 |
business_finance/ar_full_300 | Annual Report Full Extraction (300 files) | Download 300 annual reports from East Money, extract core technical personnel records, and produce one consolidated workbook with the required schema. | business_finance | Accounting & Finance | full-spectrum | Task Type:
- Full pipeline (download + parse + structure output), with file verification.
Goal:
- Download all reports listed in base\input\file_list.txt from East Money (东方财富).
- Extract core technical personnel data.
- Output one consolidated Excel file.
Input Files:
1) base\input\file_list.txt
- UTF-8 text file... | [
"Read the file list from `{task_dir}\\input\\file_list.txt` (300 PDF filenames).",
"Download all 300 annual report PDFs from East Money (dongchaiwang) to `{remote_output_dir}\\downloads`.",
"Parse each PDF to extract core technical personnel data.",
"Output a single consolidated Excel file at `{remote_output_... | [
"Google Chrome",
"Python"
] | [
{
"name": "file_list.txt",
"format": "UTF-8 text",
"path": "input/file_list.txt",
"description": "300 PDF filenames, one per line. Example: 688001_SomeCompany_2023AnnualReport.pdf"
}
] | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.1",
"subdomain_code": "accounting",
"subdomain_name": "Accounting & Finance"
} | tasks/business_finance/ar_full_300 |
business_finance/basel_operational_risk_bia_cn | Basel Operational Risk BIA Classification | Classify Chinese operational-risk loss events into Basel event categories and business lines, then calculate regulatory capital under the Basic Indicator Approach. | business_finance | Actuarial & Risk Modeling | full-spectrum | You are working on a Linux VM as an operational-risk analyst.
## Task Directory
`base`
## Visible Inputs
- Task prompt: `base/input/TASK_PROMPT.md`
- Raw Chinese loss events: `base/input/operational_risk_events.xlsx`
- Basel loss event categories: `base/input/loss_event_categories.xlsx`
- Basel business line mapping:... | [
"Read all 60 Chinese operational-risk event descriptions.",
"Assign one Basel loss event category code from `EL1` through `EL7` to each event.",
"Assign one Basel business line code from `BL1` through `BL8` to each event.",
"Calculate operational-risk regulatory capital using the Basel Basic Indicator Approac... | [
"LibreOffice Calc",
"Python",
"openpyxl"
] | [
{
"name": "TASK_PROMPT.md",
"format": "md",
"path": "input/TASK_PROMPT.md",
"description": "Solve-time task instructions and output contract."
},
{
"name": "operational_risk_events.xlsx",
"format": "xlsx",
"path": "input/operational_risk_events.xlsx",
"description": "Chinese oper... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.2",
"subdomain_code": "actuarial_risk",
"subdomain_name": "Actuarial & Risk Modeling"
} | tasks/business_finance/basel_operational_risk_bia_cn |
business_finance/bpmn_category_governance_restructuring_l3 | Flowable BPMN Category Governance Restructuring L3 | Redesign Z Global's Maternal & Baby category governance workflow in Flowable BPMN 6.5.0 for a compound organizational disruption, then produce the required BPMN, compliance, scenario-result, and design-decision artifacts. | business_finance | Enterprise Analytics & Planning | last-exam | You are working on a Linux VM with Docker, Docker Compose, Python, and curl available.
## Your Task
Redesign Z Global's Maternal & Baby category governance workflow in Flowable BPMN 6.5.0 so it handles a compound organizational disruption:
1. the M&B category owner vacancy immediately before a promotion cycle
2. tempo... | [
"Read the staged task prompt, starter BPMN, business rules, org hierarchy, scenarios, and stakeholder artifacts.",
"Modify the BPMN to handle the L3 compound governance disruption while preserving anchored original elements and IDs.",
"Use process definition key `zMBCategoryGovernance_modified_L3` and keep new ... | [
"Flowable 6.5.0",
"Docker",
"Docker Compose",
"Python",
"curl"
] | [
{
"name": "task_prompt.md",
"format": "Markdown",
"path": "input/task_prompt.md",
"description": "Agent-visible task contract"
},
{
"name": "starter_project/original_process.bpmn20.xml",
"format": "BPMN XML",
"path": "input/starter_project/original_process.bpmn20.xml",
"descripti... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.3",
"subdomain_code": "enterprise_analytics",
"subdomain_name": "Enterprise Analytics & Planning"
} | tasks/business_finance/bpmn_category_governance_restructuring_l3 |
business_finance/bpmn_supply_disruption_l3 | Flowable BPMN Supply Disruption L3 | Redesign LY Juice Company's monthly production scheduling workflow in Flowable BPMN 6.5.0 so it handles a compound supply-shortage and quality-hold disruption, then produce the required BPMN and compliance output bundle. | business_finance | Enterprise Analytics & Planning | near-term | You are working on a Linux VM with Docker, Docker Compose, Python, and curl available.
## Your Task
Redesign LY Juice Company's monthly production scheduling workflow in Flowable BPMN 6.5.0 so it handles a compound disruption:
1. raw material supply shortage
2. incoming material quality hold
## Visible Inputs
- task ... | [
"Read the staged task prompt and starter-project materials under `input/`.",
"Modify the BPMN to handle the L3 compound disruption while preserving anchored original elements and IDs.",
"Use process definition key `monthlyProductionScheduling_modified_L3` and keep new manual work as `userTask` nodes.",
"Start... | [
"Flowable 6.5.0",
"Docker",
"Docker Compose",
"Python"
] | [
{
"name": "task_prompt.md",
"format": "Markdown",
"path": "input/task_prompt.md",
"description": "Agent-visible task contract derived from the raw submission prompt with evaluator-only sections removed"
},
{
"name": "starter_project/original_process.bpmn20.xml",
"format": "BPMN XML",
... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.3",
"subdomain_code": "enterprise_analytics",
"subdomain_name": "Enterprise Analytics & Planning"
} | tasks/business_finance/bpmn_supply_disruption_l3 |
business_finance/digital_marketing_ab_test_analysis_1 | Digital Marketing A/B Test Analysis | Design and analyze a marketing A/B test with exclusion handling, stratified assignment checks, two-proportion inference, BH correction, and a final ship/hold recommendation. | business_finance | Sales & Marketing | near-term | You are working on a Linux VM to complete a digital marketing A/B test analysis.
Visible task files:
- `base/input/experiment_brief.md`
- `base/input/historical_metrics.csv`
- `base/input/eligible_population.csv`
- `base/input/exclusion_rules.yaml`
- `base/input/active_experiment_customers.csv`
- `base/input/experimen... | [] | [
"Python 3.12",
"scipy",
"statsmodels",
"pandas",
"GrowthBook"
] | [] | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.6",
"subdomain_code": "sales_marketing",
"subdomain_name": "Sales & Marketing"
} | tasks/business_finance/digital_marketing_ab_test_analysis_1 |
business_finance/digital_marketing_audience_segmentation_1 | Digital Marketing Audience Segmentation | Segment customer profiles into a high-transaction email-disengaged cohort for re-engagement via SMS and push, applying governance rules and overlap analysis. | business_finance | Sales & Marketing | full-spectrum | You are a digital marketing analyst performing audience segmentation for a re-engagement campaign.
## Task Directory
`base`
## Your Task
1. Read the segmentation brief at `base/input/segmentation_brief.md` and the governance policies at `base/input/governance_policies.yaml`. Use `base/input/data_dictionary.tsv` for f... | [
"Filter unified_profiles.parquet to high-transaction email-disengaged customers per the brief.",
"Apply governance suppression (active support tickets) and channel opt-in/out rules.",
"Strip geo/demographic PII columns (age, gender, city, state) from the output roster.",
"Compute per-customer SMS, push, and a... | [
"Python",
"pandas",
"pyarrow"
] | [
{
"name": "unified_profiles.parquet",
"format": "Parquet",
"path": "input/unified_profiles.parquet",
"description": "~10,000 customer profiles with demographics, purchase, and engagement fields."
},
{
"name": "segmentation_brief.md",
"format": "Markdown",
"path": "input/segmentation_... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.6",
"subdomain_code": "sales_marketing",
"subdomain_name": "Sales & Marketing"
} | tasks/business_finance/digital_marketing_audience_segmentation_1 |
business_finance/equity_research_summary | Equity Research Summary | Build a company equity-research workbook from staged filings and market sources, then produce the required summary outputs. | business_finance | Enterprise Analytics & Planning | full-spectrum | You are an equity research analyst preparing a one-page Tesla workbook in LibreOffice Calc.
## Variant
`tsla_fy2023`: Single Tesla equity-research workbook using live Yahoo pages plus fixed FY2023 SEC values.
## Runtime Entry Points
- Read the staged instructions: `tsla_fy2023\input\instruction.md`
- Read the allowed... | [
"Open Yahoo Finance for Tesla (`TSLA`), visit the quote page and the Statistics page, and collect the live market-data and valuation fields listed in `input/instruction.md`.",
"Open SEC EDGAR for Tesla and locate the Tesla FY2023 annual 10-K financial statements needed for the workbook.",
"Launch LibreOffice Ca... | [
"Chrome"
] | [
{
"name": "instruction.md",
"format": "Markdown",
"path": "input/instruction.md",
"description": "Sanitized runtime brief with required sections, output filename, and source workflow"
},
{
"name": "source_urls.txt",
"format": "UTF-8 text",
"path": "input/source_urls.txt",
"descri... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.3",
"subdomain_code": "enterprise_analytics",
"subdomain_name": "Enterprise Analytics & Planning"
} | tasks/business_finance/equity_research_summary |
business_finance/ff5_public_reconstruction | FF5 Public Reconstruction | Reconstruct the monthly Fama-French five-factor series from 2015-01 onward using only the staged allowlisted public sources, then save the required factor CSV. | business_finance | Enterprise Analytics & Planning | last-exam | You are working on a Linux finance benchmark task.
Read these staged files first:
- `base/input/agent_prompt.md`
- `base/input/README.md`
- `base/input/manifest.json`
Task-local entry points:
- Browser launcher: `base/software/browser`
- Python wrapper: `base/software/python`
- UV wrapper: `base/software/uv`
If you ... | [
"Read `input/agent_prompt.md`, `input/README.md`, and `input/manifest.json` before starting.",
"Stay inside the staged public-data allowlist while reading the paper, gathering data, and building the factor pipeline.",
"Optionally install task-facing Python dependencies from `input/runtime_env/` with the staged ... | [
"Google Chrome",
"Python",
"uv"
] | [
{
"name": "agent_prompt.md",
"format": "Markdown",
"path": "input/agent_prompt.md",
"description": "Primary agent-visible benchmark brief, including the allowed public sources and output contract."
},
{
"name": "README.md",
"format": "Markdown",
"path": "input/README.md",
"descri... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.3",
"subdomain_code": "enterprise_analytics",
"subdomain_name": "Enterprise Analytics & Planning"
} | tasks/business_finance/ff5_public_reconstruction |
business_finance/financial_stmt_reconstruction_aapl_fy2024 | Apple FY2024 Balance Sheet Reconstruction | Extract Apple Inc.'s FY2024 Consolidated Balance Sheet from its Form 10-K and return a structured JSON report with exact USD-million values. | business_finance | Accounting & Finance | near-term | You are working on a Linux VM as a financial statement extraction analyst.
## Task Directory
`base`
## Visible Inputs
- Task prompt: `base/input/TASK_PROMPT.md`
- Apple FY2024 10-K PDF: `base/input/aapl-2024-10k.pdf`
- SEC EDGAR HTML: `base/input/aapl-20240928.htm`
- Task metadata: `base/input/task.json`
- Output sch... | [
"Locate the Consolidated Balance Sheets in the staged Apple FY2024 Form 10-K materials.",
"Extract the September 28, 2024 value for every required field in the schema.",
"Write `output/balance_sheet.json` without modifying staged input files."
] | [
"Python",
"pdftotext",
"grep"
] | [
{
"name": "aapl-2024-10k.pdf",
"format": "pdf",
"path": "input/aapl-2024-10k.pdf",
"description": "Cached Apple FY2024 Form 10-K PDF."
},
{
"name": "aapl-20240928.htm",
"format": "html",
"path": "input/aapl-20240928.htm",
"description": "SEC EDGAR HTML filing for the same 10-K."
... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.1",
"subdomain_code": "accounting",
"subdomain_name": "Accounting & Finance"
} | tasks/business_finance/financial_stmt_reconstruction_aapl_fy2024 |
business_finance/internal_employee_agent_instance_1 | Internal Employee Agent Conversation Replay | Read staged HR and IT support conversations, then produce one deterministic `results.json` that captures the correct per-turn responses and tool usage across all sessions. | business_finance | HR & Project Management | full-spectrum | You are working on a Linux VM.
## Your Task
Act as the company's internal HR / IT assistant across all staged conversation sessions and write the final structured artifact to `base/output/results.json`.
## Visible Inputs
- agent rules: `base/input/agent_rules.md`
- HR knowledge base: `base/input/hr_knowledge_base.md`... | [
"Read the staged rules, knowledge bases, conversation sessions, and deterministic web-search grounding.",
"Process each test id in `queries.json` as an independent multi-turn session while preserving turn order and within-session memory.",
"Write exactly one `results.json` file under `output/`.",
"Use the exa... | [
"Python"
] | [
{
"name": "agent_rules.md",
"format": "Markdown",
"path": "input/agent_rules.md",
"description": "Canonical tool names and operational rules for the HR / IT assistant."
},
{
"name": "hr_knowledge_base.md",
"format": "Markdown",
"path": "input/hr_knowledge_base.md",
"description":... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.5",
"subdomain_code": "hr_pm",
"subdomain_name": "HR & Project Management"
} | tasks/business_finance/internal_employee_agent_instance_1 |
business_finance/legal_ma_consistency_audit_01 | M&A Filing Consistency Audit | Review four Chinese regulatory filings about the same share-transfer transaction and write an English audit report that identifies supported inconsistencies with Chinese evidence and location markers. | business_finance | Litigation Support & Discovery | last-exam | You are reviewing four Chinese regulatory filings about the same share-transfer transaction.
## Task
Read the staged materials, cross-check the filings for factual inconsistencies, and write one
English audit report.
## Visible Inputs
- Task root: `base`
- Task brief: `base/input/task_brief.md`
- Document manifest: `... | [
"Read the staged task brief, document manifest, source PDFs, and extracted-text mirrors.",
"Cross-check the four filings for material factual inconsistencies.",
"Write exactly one English Markdown audit report to `output/audit_report.md`.",
"For every finding, include the original Chinese evidence text and a ... | [
"software/python"
] | [
{
"name": "task_brief.md",
"format": "Markdown",
"path": "input/task_brief.md",
"description": "Agent-visible task instructions and output contract."
},
{
"name": "document_manifest.json",
"format": "JSON",
"path": "input/document_manifest.json",
"description": "Maps the canonica... | {
"domain_id": "7",
"domain_code": "legal",
"subdomain_id": "7.1",
"subdomain_code": "litigation",
"subdomain_name": "Litigation Support & Discovery"
} | tasks/business_finance/legal_ma_consistency_audit_01 |
business_finance/llm_ecosystem_privacy_audit_realdata_1 | GPT Actions Privacy & Policy Audit | Audit 2,253 real GPT Actions against OpenAI's published GPT Actions Usage Policy and compute cross-domain data-exposure amplification across shared backend domains on a Linux VM. | business_finance | Compliance & Regulatory | full-spectrum | You are working on a Linux VM to perform a compliance audit of GPT Actions against OpenAI's published GPT Actions Usage Policy. All inputs are staged locally on this VM. No network access is required or permitted.
## Input Files (read-only)
- Pre-classified Action data: `base/input/pp_action_data_entities.json`
- Data... | [
"Parse `pp_action_data_entities.json`, `taxonomy.csv`, `openai_gpt_actions_usage_policy.md`, and `gpt_action_metadata.json` with only Python stdlib modules.",
"Apply the Policy-to-Taxonomy Mapping Guide verbatim to flag CRITICAL and HIGH taxonomy types per Action field.",
"Write `output/policy_violations.json` ... | [
"Python (`software/python`)"
] | [
{
"name": "pp_action_data_entities.json",
"format": "JSON",
"path": "input/pp_action_data_entities.json",
"description": "Pre-classified data collection profiles for 2,253 GPT Actions, keyed by `\"<backend_domain>, <api_name>\"`. Each Action lists its data fields with an assigned taxonomy `data_type... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.4",
"subdomain_code": "compliance",
"subdomain_name": "Compliance & Regulatory"
} | tasks/business_finance/llm_ecosystem_privacy_audit_realdata_1 |
business_finance/metabase_bi_dashboard_01 | Metabase BI Dashboard | Build a business analytics dashboard in Metabase using a pre-loaded SQLite database, then export key metrics as a structured JSON report. | business_finance | Enterprise Analytics & Planning | near-term | You are a business analyst building a BI dashboard in Metabase.
## Environment
- Metabase is available on this Windows machine. To start it, run the launcher script:
`base\software\launch_metabase.bat`
Then wait for the server to become ready and open http://localhost:3000 in the browser.
- Login credentials are i... | [
"Launch Metabase via the launcher script and wait for the server to start",
"Log in at http://localhost:3000 with the provided credentials",
"Build a dashboard with 6 charts and a heading text card in the Metabase GUI",
"Query the Business Analytics SQLite database to extract all required metrics",
"Save da... | [
"Metabase 0.54.3",
"Microsoft Edge"
] | [
{
"name": "Task requirements",
"format": "md",
"path": "input/requirements.md",
"description": "Detailed dashboard requirements specifying 6 charts and a heading text card."
},
{
"name": "Output schema",
"format": "json",
"path": "input/output_schema.json",
"description": "JSON S... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.3",
"subdomain_code": "enterprise_analytics",
"subdomain_name": "Enterprise Analytics & Planning"
} | tasks/business_finance/metabase_bi_dashboard_01 |
business_finance/odoo | Odoo Supply-Chain End-To-End Workflow | Execute a full Odoo ERP supply-chain workflow — purchases, landed cost, subcontracting, two-level manufacturing, delivery, invoicing, and a returns / credit-note flow — and leave the final business state inside the variant's PostgreSQL database while saving evidence screenshots. | business_finance | Supply Chain & Logistics | full-spectrum | Goal:
Complete the recovered end-to-end Odoo supply-chain workflow and leave the final business state inside the task database.
Variant:
- odoo_compact
- Recovered compact legacy variant. The shared Odoo workflow is preserved, with this variant using its own DB and searchable run tag.
Recovered prompt:
Recovered comp... | [
"Launch Odoo through `software/launch_odoo.cmd` or the auto-opened browser session, and work exclusively inside the variant database (for example `odoo_compact`).",
"Follow the full workflow staged in `input/prompt.txt`: master data, tagged sales / purchases, landed cost, subcontracting, two-level manufacturing w... | [
"Odoo 19",
"PostgreSQL 17"
] | [
{
"name": "Agent-visible workflow prompt",
"format": "`.txt`",
"path": "input/prompt.txt",
"description": "Full recovered workflow contract for the active variant — master data codes, FX rates, warehouse routes, tagged purchases, subcontracting, shortage recovery, invoicing, expenses, and the mandat... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.8",
"subdomain_code": "supply_chain",
"subdomain_name": "Supply Chain & Logistics"
} | tasks/business_finance/odoo |
business_finance/pe_screening_memo_1 | PE Screening Memo 1 | Review a staged Zscaler diligence packet and write a structured private-equity screening memo with an explicit investment recommendation. | business_finance | Accounting & Finance | near-term | You are working on a Linux finance benchmark task.
Read these staged files first:
- `zscaler_fy2025/input/task_brief.md`
- `zscaler_fy2025/input/memo_template.md`
- `zscaler_fy2025/input/source_manifest.json`
Task-local software notes:
- `zscaler_fy2025/software/README.txt`
Your job is to write the requested private... | [
"Read the staged brief, memo template, and source manifest before starting.",
"Use only the staged Zscaler packet and do not use web search.",
"Write a Markdown screening memo that follows the visible template exactly enough to preserve the required section structure.",
"Make an explicit `Go`, `No-Go`, or `Ho... | [
"Python"
] | [
{
"name": "task_brief.md",
"format": "Markdown",
"path": "input/task_brief.md",
"description": "Primary agent-visible benchmark brief with the output contract and workflow constraints."
},
{
"name": "memo_template.md",
"format": "Markdown",
"path": "input/memo_template.md",
"desc... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.1",
"subdomain_code": "accounting",
"subdomain_name": "Accounting & Finance"
} | tasks/business_finance/pe_screening_memo_1 |
business_finance/saas_onepager_brand_refresh_instance_1 | SaaS One-Pager Brand Refresh | Rebuild a flattened SaaS one-pager as an editable single-slide PowerPoint on Windows, apply the staged NorthstarOS rebrand changes, and export both a PPTX and a PNG. | business_finance | Graphic, Visual & Product Design | near-term | You are editing a SaaS one-pager on a Windows VM.
## Variant
`base`: NorthstarOS one-pager brand refresh
## Runtime Entry Point
- Open PowerPoint from: `base\software\OpenPowerPoint.bat`
## Visible Inputs
- Source one-pager PNG: `base\input\original_onepager.png`
- Edit brief: `base\input\edit_request.txt`
- Brand g... | [
"Read the staged source one-pager, edit brief, brand guide, KPI CSV, chart CSV, and asset bundle from `input/`.",
"Rebuild the page as a meaningfully editable single-slide PowerPoint on Windows.",
"Apply the requested NorthstarOS brand refresh while preserving the overall composition.",
"Save exactly `output/... | [
"Microsoft PowerPoint"
] | [
{
"name": "original_onepager.png",
"format": "PNG image",
"path": "input/original_onepager.png",
"description": "Flattened source one-pager to recreate and refresh."
},
{
"name": "edit_request.txt",
"format": "text",
"path": "input/edit_request.txt",
"description": "Natural-langu... | {
"domain_id": "8",
"domain_code": "visual_media",
"subdomain_id": "8.2",
"subdomain_code": "graphic_design",
"subdomain_name": "Graphic, Visual & Product Design"
} | tasks/business_finance/saas_onepager_brand_refresh_instance_1 |
business_finance/sec_10k_financial_parsing | SEC 10-K Financial Parsing | Parse a fixed 100-filing SEC 10-K corpus into normalized financial JSON outputs, preserve the raw text evidence used for extraction, answer three cross-filing analytical questions, and rerun a fixed validation subset deterministically. | business_finance | Accounting & Finance | last-exam | You are working on a Linux VM with a fixed corpus of SEC 10-K PDFs.
Visible files and tools:
- Filing corpus: `base/input/filings`
- Filing metadata: `base/input/filings/manifest.json`
- Output schema: `base/input/schema/extraction_schema.json`
- Normalization rules: `base/input/schema/normalization_rules.txt`
- Analy... | [
"Inspect the staged SEC 10-K corpus in `input/filings/` and use `input/filings/manifest.json` to understand company, ticker, and year coverage.",
"Produce one normalized extraction JSON per filing under `output/extractions/`, following the staged schema and normalization rules exactly.",
"Save one raw extractio... | [
"Python",
"uv",
"pdfplumber",
"pypdf",
"pydantic"
] | [
{
"name": "filings/",
"format": "Directory of PDFs",
"path": "input/filings/",
"description": "Fixed 100-filing SEC 10-K corpus."
},
{
"name": "manifest.json",
"format": "JSON",
"path": "input/filings/manifest.json",
"description": "Filing metadata covering ticker, company name, ... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.1",
"subdomain_code": "accounting",
"subdomain_name": "Accounting & Finance"
} | tasks/business_finance/sec_10k_financial_parsing |
business_finance/sse_northbound_programmatic_trading_01 | SSE Northbound Programmatic Trading Research | Answer three closed-book Shanghai Stock Exchange reporting questions from staged Chinese source documents and return a structured JSON memo with Chinese citations and evidence. | business_finance | Compliance & Regulatory | near-term | You are acting as a regulatory consultant on Shanghai Stock Exchange Northbound programmatic-trading reporting.
## Closed-Book Rule
- Use only the staged materials under `base/input`.
- Do not use outside web search or external regulatory knowledge.
## Inputs
- Task brief: `base/input/task_brief.md`
- Question set:... | [
"Read the staged task brief, question set, output contract, document manifest, source documents, and extracted-text mirrors.",
"Answer all three client questions using only the staged Shanghai Stock Exchange materials.",
"Write exactly one JSON file to `output/research_answers.json`.",
"For each question, pro... | [
"Python"
] | [
{
"name": "task_brief.md",
"format": "Markdown",
"path": "input/task_brief.md",
"description": "Agent-visible task instructions and the three client questions."
},
{
"name": "question_set.json",
"format": "JSON",
"path": "input/question_set.json",
"description": "Structured list ... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.4",
"subdomain_code": "compliance",
"subdomain_name": "Compliance & Regulatory"
} | tasks/business_finance/sse_northbound_programmatic_trading_01 |
business_finance/taxform_4_1 | Taxform 4 1 | Prepare a U.S. tax return from staged wage documents and instructions, then save the required JSON outputs. | business_finance | Accounting & Finance | near-term | You are preparing a U.S. tax return inside a browser-based local task environment.
## Variant
`variant_1`: W-2 income with a single Form 1040 output.
## Runtime Entry Points
- Start the local HTTP server by running: `variant_1\software\start_http_server.bat`
- Read the taxpayer instructions at: `variant_1\input\instr... | [
"Launch the task-local HTTP server from `software/start_http_server.bat`, which serves the staged `input/` directory at `http://localhost:8080`.",
"Read `input/instruction.txt` and the staged source forms listed in `input/README.md`, combining free-text tax instructions with values shown on the pre-filled W-2 / 1... | [
"Python",
"Chrome"
] | [
{
"name": "README.md",
"format": "Markdown",
"path": "input/README.md",
"description": "Source-authored setup and page-order instructions"
},
{
"name": "instruction.txt",
"format": "UTF-8 text",
"path": "input/instruction.txt",
"description": "Taxpayer scenario, filing rules, and... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.1",
"subdomain_code": "accounting",
"subdomain_name": "Accounting & Finance"
} | tasks/business_finance/taxform_4_1 |
computing_math/branch_bound_atsp | Branch-and-Bound ATSP Solver | Implement a Python branch-and-bound solver for deterministic asymmetric TSP instances and report certified results across three tiers. | computing_math | Mathematical & Operations Research | near-term | You are working on a Linux VM.
## Task Directory
`base`
## Visible Inputs
- Task prompt: `base/input/TASK_PROMPT.md`
- ATSP problem specification: `base/input/problem_spec.md`
- Runtime manifest: `base/input/runtime_env/pyproject.toml`
- Python entry point with task dependencies: `base/software/python`
## Your Task
... | [
"Regenerate the deterministic 3D city coordinates for the 12, 20, and 35 city tiers.",
"Implement branch-and-bound and assignment-relaxation based lower bounds without external optimization solvers.",
"Write a valid `results.json` containing tours, costs, bounds, gaps, node counts, and runtime metrics."
] | [
"Python",
"NumPy",
"SciPy"
] | [
{
"name": "problem_spec.md",
"format": "markdown",
"path": "input/problem_spec.md",
"description": "Authoritative ATSP problem statement and output schema."
},
{
"name": "TASK_PROMPT.md",
"format": "markdown",
"path": "input/TASK_PROMPT.md",
"description": "Normalized solve-time ... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.4",
"subdomain_code": "math_ops_research",
"subdomain_name": "Mathematical & Operations Research"
} | tasks/computing_math/branch_bound_atsp |
computing_math/cfr_game_theory_equilibrium | CFR Game Theory Equilibrium | Implement exact and regret-minimization solvers for three zero-sum games and write one combined JSON output. | computing_math | Mathematical & Operations Research | last-exam | You are working on a Linux VM.
## Your Task
Implement equilibrium solvers for three progressively harder two-player zero-sum games and save one combined `results.json`.
## Visible Input
- Authoritative problem specification: `base/input/problem_spec.md`
- Solve-time runtime manifest: `base/input/runtime_env/pyproject... | [
"Read the staged problem specification and implement all three tiers of the benchmark.",
"Use the staged runtime wrappers to install and run the NumPy-only solve-time environment if needed.",
"Return one combined `results.json` containing Tier 1 matrix-game, Tier 2 Kuhn CFR, and Tier 3 Leduc MCCFR outputs."
] | [
"Python",
"uv",
"NumPy"
] | [
{
"name": "Problem specification",
"format": "Markdown",
"path": "input/problem_spec.md",
"description": "Agent-visible task specification for all three game-solving tiers."
},
{
"name": "Runtime manifest",
"format": "pyproject.toml",
"path": "input/runtime_env/pyproject.toml",
"... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.4",
"subdomain_code": "math_ops_research",
"subdomain_name": "Mathematical & Operations Research"
} | tasks/computing_math/cfr_game_theory_equilibrium |
computing_math/clustered_cyclic_code_circuit_level_simulation | Clustered-Cyclic Code Circuit Simulation | Reproduce circuit-level logical failure-rate data for three clustered-cyclic CSS quantum error-correcting codes using the supplied construction notes and simulation grid. | computing_math | Quantum Computing | full-spectrum | You are working on a Linux VM.
## Variant
`base`: Three clustered-cyclic CSS codes with QUITS/Stim circuit-level memory simulation.
## Input Files
- Task notes: `base/input/cc_codes_quits_extraction.tex`
- Simulation grid: `base/input/simulation_grid.csv`
- Output requirements: `base/input/output_requirements.txt`
- ... | [
"Read `input/cc_codes_quits_extraction.tex`, `input/simulation_grid.csv`, and `input/output_requirements.txt`.",
"Install or inspect the open-source Python runtime from `input/runtime_env/pyproject.toml` or `input/requirements.txt`.",
"Construct and simulate the `[24,8,3]`, `[40,8,5]`, and `[54,18,3]` clustered... | [
"Python",
"Stim",
"ldpc",
"QUITS",
"NumPy",
"pandas"
] | [
{
"name": "cc_codes_quits_extraction.tex",
"format": "tex",
"path": "input/cc_codes_quits_extraction.tex",
"description": "Agent-facing construction and simulation notes from the submitter."
},
{
"name": "simulation_grid.csv",
"format": "csv",
"path": "input/simulation_grid.csv",
... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.7",
"subdomain_code": "quantum",
"subdomain_name": "Quantum Computing"
} | tasks/computing_math/clustered_cyclic_code_circuit_level_simulation |
computing_math/cost_optimization_1 | AWS Cost Optimization Analysis | Analyze a synthetic AWS billing dashboard screenshot plus usage and pricing tables to identify wasteful resources and produce a structured optimization report with projected monthly savings. | computing_math | Infrastructure Engineering & Cloud Operations | full-spectrum | You are an AWS cost optimization analyst working on Linux.
## Task Directory
`base`
## Visible Inputs
- Task brief: `base/input/task_description.txt`
- Dashboard image: `base/input/aws_billing_dashboard.png`
- Structured usage data: `base/input/resource_usage.csv`
- Pricing table: `base/input/aws_pricing_reference.cs... | [
"Read `input/task_description.txt` and inspect the dashboard image directly.",
"Use the CSV inputs plus the 3 dashboard-only findings to identify wasteful EC2, RDS, S3, NAT Gateway, Elastic IP, and CloudWatch spend.",
"Apply the explicit pricing and optimization rules from the task brief using `input/aws_pricin... | [
"Python",
"pandas",
"uv"
] | [
{
"name": "aws_billing_dashboard.png",
"format": "PNG",
"path": "input/aws_billing_dashboard.png",
"description": "Synthetic AWS billing dashboard screenshot containing both structured tables and 3 dashboard-only findings that are not present in the usage CSV."
},
{
"name": "resource_usage.c... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.6",
"subdomain_code": "infra_cloud",
"subdomain_name": "Infrastructure Engineering & Cloud Operations"
} | tasks/computing_math/cost_optimization_1 |
computing_math/cp_test_gen_1 | Competitive Programming Test Generator — Binary String Copying | Write gen.cpp, a C++ test case generator for the Binary String Copying problem that produces adversarial inputs to distinguish correct from incorrect solutions among 10 hidden submissions. | computing_math | Software Engineering | near-term | You are tasked with writing gen.cpp, a C++ program that prints one complete valid test case to stdout per invocation and accepts a single integer seed as argv[1] (e.g. ./gen 1, ./gen 2, ./gen 50) to vary output across invocations.
The problem statement is provided in Binary String Copying Problem Statement.pdf. The 10... | [
"Read the Binary String Copying problem statement PDF and submission IDs from the xlsx.",
"Analyze the problem structure to identify adversarial test case strategies (TLE, WA, boundary conditions).",
"Write gen.cpp that generates varied test cases across 50 seeds respecting all constraints.",
"Compile and ver... | [
"g++ (C++17)",
"bash"
] | [
{
"name": "Binary String Copying Problem Statement.pdf",
"format": "pdf",
"path": "input/Binary String Copying Problem Statement.pdf",
"description": "Full Codeforces problem statement with constraints."
},
{
"name": "Binary String Copying Inputs.xlsx",
"format": "xlsx",
"path": "inp... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.1",
"subdomain_code": "software_eng",
"subdomain_name": "Software Engineering"
} | tasks/computing_math/cp_test_gen_1 |
computing_math/data_pipeline_etl_instance_1 | Retail ETL Warehouse Cleanup | Build a cleaned SQLite star-schema warehouse plus two truthful JSON sidecars from messy retail CSV, JSON, and TSV source files on a Linux VM. | computing_math | Data & Analytics Engineering | full-spectrum | You are working on a Linux VM to build a cleaned SQLite warehouse from messy retail source files.
Visible task files:
- `base/input/task_prompt.md`
- `base/input/target_schema_spec.json`
- `base/input/output_contract.json`
- `base/input/raw_transactions/`
- `base/input/raw_customers/customers.json`
- `base/input/raw_p... | [
"Read the staged prompt, schema spec, output contract, and messy retail source files under `input/`.",
"Build a cleaned SQLite warehouse with deduplication, null handling, standardization, surrogate keys, and a full `dim_dates` table.",
"Write `output/warehouse.db`, `output/data_quality_report.json`, and `outpu... | [
"Python 3.10",
"uv",
"pandas",
"SQLite"
] | [
{
"name": "task_prompt.md",
"format": "Markdown",
"path": "input/task_prompt.md",
"description": "Agent-visible task brief for the ETL workflow."
},
{
"name": "target_schema_spec.json",
"format": "JSON",
"path": "input/target_schema_spec.json",
"description": "Semantic warehouse ... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.2",
"subdomain_code": "data_analytics",
"subdomain_name": "Data & Analytics Engineering"
} | tasks/computing_math/data_pipeline_etl_instance_1 |
computing_math/dit_pipeline_cfg_alignment_fid_256_001 | Repair Standalone DiT Pipeline CFG Alignment | Repair a buggy standalone DiT sampling pipeline so its classifier-free guidance behavior matches the intended reference implementation while preserving the one-file edit workflow. | computing_math | AI Engineering, Safety & CS Research | full-spectrum | You are repairing a standalone Diffusers-based DiT pipeline on a Linux VM.
Task directory:
- `base`
Visible files:
- Buggy starter pipeline: `base/input/pipeline_dit.py`
- Raw sampling harness context: `base/input/sample.py`
- Raw run script context: `base/input/run_sample.sh`
- Benchmark-owned prompt: `base/input/ta... | [
"Read the benchmark-owned task prompt and inspect the buggy standalone `pipeline_dit.py` plus the raw context files `sample.py` and `run_sample.sh`.",
"Repair the standalone DiT pipeline so the guidance behavior matches the intended reference behavior.",
"Keep the fix localized to the pipeline file rather than ... | [
"Python",
"Diffusers",
"PyTorch"
] | [
{
"name": "pipeline_dit.py",
"format": "Python",
"path": "input/pipeline_dit.py",
"description": "Buggy standalone starter pipeline."
},
{
"name": "sample.py",
"format": "Python",
"path": "input/sample.py",
"description": "Raw sampling harness context from the submission."
},
... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.3",
"subdomain_code": "ai_cs_research",
"subdomain_name": "AI Engineering, Safety & CS Research"
} | tasks/computing_math/dit_pipeline_cfg_alignment_fid_256_001 |
computing_math/ghidra_malware_config_extraction_01 | Ghidra Malware Config Extraction | Reverse engineer a packed Windows malware sample in Ghidra and recover the configuration fields requested by the visible JSON schema. | computing_math | Cybersecurity & Digital Forensics | near-term | You are a malware analyst working on a Windows VM.
## Your Task
Use Ghidra to reverse engineer the staged executable and recover the malware configuration requested by the visible schema.
## Visible Files
- Sample to analyze: `base\input\sample.exe`
- Output schema: `base\input\output_schema.json`
- Launch Ghidra fro... | [
"Launch the staged task-local Ghidra runtime from `software/launch_ghidra.bat`.",
"Import `input/sample.exe` into a local non-shared Ghidra project.",
"Recover the packer, C2, obfuscation, and architecture details requested by the visible schema.",
"Save exactly one schema-valid `output/malware_config.json`."... | [
"Ghidra 11.3",
"JDK 21"
] | [
{
"name": "Packed malware sample",
"format": "Windows PE",
"path": "input/sample.exe",
"description": "The executable the agent must reverse engineer in Ghidra."
},
{
"name": "Visible output schema",
"format": "JSON",
"path": "input/output_schema.json",
"description": "Defines th... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.5",
"subdomain_code": "cybersecurity",
"subdomain_name": "Cybersecurity & Digital Forensics"
} | tasks/computing_math/ghidra_malware_config_extraction_01 |
computing_math/go_game_reconstruction_1 | Go Game Reconstruction 1 | Reconstruct a professional 19x19 Go game inside Sabaki from a final board image, five known opening moves, and game metadata, then export the result as SGF. | computing_math | Sports | full-spectrum | You are reconstructing a professional 19x19 Go game in Sabaki on Ubuntu.
## Your Task
Use the final board image plus the known metadata to reconstruct the game move by move inside Sabaki, then export the reconstructed game as SGF.
## Visible Inputs
- Final board image: `base/input/input-board-position.png`
- Sabaki A... | [
"Inspect the staged final-board image under `input/`.",
"Launch the staged Sabaki AppImage on the Ubuntu VM and use the GUI rather than bypassing the task with a browser or Go engine.",
"Reconstruct the move sequence using the visible board image, five fixed opening moves, and the provided metadata.",
"Export... | [
"Sabaki v0.52.2",
"Python",
"sgfmill"
] | [
{
"name": "Final board image",
"format": "PNG image",
"path": "input/input-board-position.png",
"description": "The final 19x19 board state shown to the agent."
},
{
"name": "Sabaki runtime",
"format": "Linux AppImage",
"path": "software/sabaki-v0.52.2-linux-x64.AppImage",
"descr... | {
"domain_id": "14",
"domain_code": "other",
"subdomain_id": "14.1",
"subdomain_code": "sports",
"subdomain_name": "Sports"
} | tasks/computing_math/go_game_reconstruction_1 |
computing_math/ising_post_measurement_1 | Post-Measurement States for a Quantum Ising Chain | Compute the post-measurement output bundle for several 1D quantum Ising chain variants, including the critical ground state, measurement probabilities, one-site reduced density matrices, and optional one-body correlators. | computing_math | Quantum Computing | near-term | You are working on a Linux VM.
## Variant
`n10_critical_u01_correlators`: N=10, u=0.1, critical ancilla equal to the computed ground state, with one-body correlators
## Input Files
- Task specification: `n10_critical_u01_correlators/input/task_specification.md`
- Numerical parameters: `n10_critical_u01_correlators/in... | [
"Read the staged task specification and variant parameters from `input/task_specification.md` and `input/config.json`.",
"Use the staged `ancilla_state.npy` only for the paramagnetic-ancilla variants; otherwise follow the critical-ancilla rule from the visible task spec.",
"Optionally install task-specific Pyth... | [
"Python",
"NumPy",
"SciPy",
"uv"
] | [
{
"name": "config.json",
"format": "JSON",
"path": "input/config.json",
"description": "Variant-specific numerical parameters such as system size, coupling strength, ancilla mode, and site index."
},
{
"name": "task_specification.md",
"format": "Markdown",
"path": "input/task_specifi... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.7",
"subdomain_code": "quantum",
"subdomain_name": "Quantum Computing"
} | tasks/computing_math/ising_post_measurement_1 |
computing_math/k3_abelian_extensions | Finite Abelian Extension Classification | Solve a staged finite abelian group extension classification problem in GAP and produce the exact canonical JSON answer. The benchmark contains 6 variants with different abelian groups H and search ranges for m. | computing_math | Mathematical & Operations Research | near-term | You are working on a Linux VM.
## Your Task
Solve the staged finite abelian group extension classification problem for variant `h_4_4_4_m_1_8` ((Z/4)^3, m=1..8).
## Visible Files
- Variant config: `h_4_4_4_m_1_8/input/config.json`
- Mathematical task specification: `h_4_4_4_m_1_8/input/task_specification.md`
- GAP la... | [
"Read the staged `input/config.json` and `input/task_specification.md` for the active variant.",
"Use GAP to enumerate every finite abelian group `G` that fits into the required short exact sequences across the full `m` search range.",
"Determine the `product_type` classification for every valid extension group... | [
"GAP",
"Python"
] | [
{
"name": "config.json",
"format": "JSON",
"path": "input/config.json",
"description": "Variant parameters defining `H_invariant_factors`, `H_order`, and the inclusive `m_search_range`."
},
{
"name": "task_specification.md",
"format": "Markdown",
"path": "input/task_specification.md"... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.4",
"subdomain_code": "math_ops_research",
"subdomain_name": "Mathematical & Operations Research"
} | tasks/computing_math/k3_abelian_extensions |
computing_math/k8s_migration_1 | Docker Compose To Kubernetes Migration | Migrate a 3-tier Docker Compose web app (React, Flask, PostgreSQL) to a production-grade Helm chart on local Minikube, plus matching Terraform and a CI/CD pipeline; deliverables are scored on a weighted Helm/Terraform/CI/CD/verification rubric. | computing_math | Infrastructure Engineering & Cloud Operations | near-term | You are migrating a Docker Compose 3-tier web application (React frontend, Flask backend, PostgreSQL) to a production-grade Kubernetes deployment on a local Minikube cluster.
## Working Directory
Task root on this VM:
- `base`
Agent-visible inputs:
- Compose source + app code: `base/input/app/`
- Architecture spec: ... | [
"Read `input/requirements.md` and `input/README.md` first; these list the architectural targets and which resources, replicas, probes, and policies must end up in the Helm chart.",
"Use the task-local wrappers under `software/` instead of raw PATH commands: `software/docker`, `software/minikube`, `software/kubect... | [
"Docker",
"Minikube",
"kubectl",
"Helm",
"Terraform",
"Trivy",
"Python"
] | [
{
"name": "Architecture spec",
"format": "markdown",
"path": "input/requirements.md",
"description": "Detailed Kubernetes spec: replicas, resource limits, HPA, NetworkPolicy, probes, CI/CD stages."
},
{
"name": "Task README",
"format": "markdown",
"path": "input/README.md",
"desc... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.6",
"subdomain_code": "infra_cloud",
"subdomain_name": "Infrastructure Engineering & Cloud Operations"
} | tasks/computing_math/k8s_migration_1 |
computing_math/k8s_payment_api_root_cause_analysis | Kubernetes Payment API Root Cause Analysis | Analyze static Kubernetes incident artifacts for a flapping payment-api deployment and produce a grounded JSON root-cause report. | computing_math | Data & Analytics Engineering | near-term | You are the on-call SRE for a Kubernetes production incident.
## Task Directory
`base`
## Visible Inputs
- Task prompt: `base/input/task_prompt.md`
- Cluster state capture: `base/input/cluster_state.txt`
- Current Deployment and HPA YAML: `base/input/deployment.yaml`
- Crashing pod log: `base/input/failing_pod.log`
... | [
"Read the task prompt and three static incident artifacts.",
"Identify Kubernetes root causes and cite literal evidence from the input files.",
"List affected resources and prioritized remediation steps.",
"Write output/root_cause_analysis.json."
] | [
"Python"
] | [
{
"name": "task_prompt.md",
"format": "md",
"path": "input/task_prompt.md",
"description": "Sanitized task instructions."
},
{
"name": "cluster_state.txt",
"format": "txt",
"path": "input/cluster_state.txt",
"description": "Captured kubectl pod, event, top, deployment, and HPA ou... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.2",
"subdomain_code": "data_analytics",
"subdomain_name": "Data & Analytics Engineering"
} | tasks/computing_math/k8s_payment_api_root_cause_analysis |
computing_math/mp_checkpoint_consolidation_v2 | Checkpoint Consolidation V2 | Infer the non-standard TP/PP/EP checkpoint layout from staged framework code and shard files, then reconstruct a single HuggingFace-compatible `model.safetensors` artifact. | computing_math | AI Engineering, Safety & CS Research | near-term | You are working on a Linux checkpoint-consolidation task.
## Task Directory
`base`
## Visible Inputs
- Checkpoint shards: `base/input/checkpoints`
- Framework source: `base/input/framework`
- Reference model code: `base/input/reference_model/model.py`
- Reference model config: `base/input/reference_model/config.json`... | [
"Inspect the staged framework code and shard filenames to infer the checkpoint layout.",
"Reconstruct one single-device checkpoint compatible with the staged reference model.",
"Save exactly one file named `model.safetensors` under the active variant's `output/` directory.",
"Produce a checkpoint that matches... | [
"Python",
"PyTorch",
"safetensors",
"uv"
] | [
{
"name": "Checkpoint shards",
"format": "PyTorch .pt files",
"path": "input/checkpoints/",
"description": "The staged TP/PP/EP shard files for the active variant."
},
{
"name": "Framework source",
"format": "Python",
"path": "input/framework/",
"description": "The visible source... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.3",
"subdomain_code": "ai_cs_research",
"subdomain_name": "AI Engineering, Safety & CS Research"
} | tasks/computing_math/mp_checkpoint_consolidation_v2 |
computing_math/newyear_keygen2 | Newyear Keygen2 | Reverse engineer the bundled program, recover the key-generation logic, and write the correct answer for the staged UID. | computing_math | Cybersecurity & Digital Forensics | last-exam | Provided UID:
- `20252025`
Goal:
Reverse the bundled Windows crackme and compute the correct password for the provided UID.
Inputs:
- Executable to analyze: base\input\crackme.exe
Output:
- Save EXACTLY one UTF-8 text file to: base\output\key.txt
- The file must contain exactly one line in the form `flag{...}`
- Do ... | [
"Reverse the bundled crackme.",
"Use the provided UID `20252025`.",
"Compute the password for the most recent UTC half-hour slot.",
"Write exactly one line to `output/key.txt`."
] | [
"Python"
] | [
{
"name": "crackme.exe",
"format": "Windows PE",
"path": "input/crackme.exe",
"description": "agent-visible crackme"
}
] | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.5",
"subdomain_code": "cybersecurity",
"subdomain_name": "Cybersecurity & Digital Forensics"
} | tasks/computing_math/newyear_keygen2 |
computing_math/os_log_permission_guard_v1 | OS Log Permission Guard | Safely update sandboxed Linux log-file permissions from a filesystem snapshot while preserving protected system-owner, active-writer, and non-log files. | computing_math | Infrastructure Engineering & Cloud Operations | near-term | You are working on a Linux VM.
## Task Directory
`base`
## Visible Inputs
- Filesystem snapshot: `base/input/fs_snapshot.tar.gz`
- Ownership metadata: `base/input/ownership.csv`
- Initial permission metadata: `base/input/permissions.csv`
- Active-writer exclusion list: `base/input/active_writers.json`
- Workspace set... | [
"Read the staged task instructions and metadata.",
"Create a writable sandbox workspace under `output/sandbox_fs`.",
"Change eligible `.log` files to mode `444` while leaving protected files unchanged.",
"Write `output/final_state.json` with final metadata for every listed file."
] | [
"Bash",
"GNU coreutils",
"tar",
"Python"
] | [
{
"name": "fs_snapshot.tar.gz",
"format": "tar.gz",
"path": "input/fs_snapshot.tar.gz",
"description": "Sandbox filesystem snapshot containing `/var/logs` files."
},
{
"name": "ownership.csv",
"format": "csv",
"path": "input/ownership.csv",
"description": "Owner/group metadata so... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.6",
"subdomain_code": "infra_cloud",
"subdomain_name": "Infrastructure Engineering & Cloud Operations"
} | tasks/computing_math/os_log_permission_guard_v1 |
computing_math/paper_reproduction_instance_1 | LCA-on-the-Line Table 2 Reproduction | Reproduce the 40-cell correlation table (Table 2) from the ICML 2024 paper "LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies" (arXiv:2407.16067). The provided codebase ships precomputed per-model metric dictionaries sufficient to regenerate all 40 R² / Pearson correlations without ... | computing_math | AI Engineering, Safety & CS Research | near-term | You are reproducing Table 2 of the ICML 2024 paper "LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies" (arXiv:2407.16067).
## Inputs (under `input/`)
- `paper.pdf` - local copy of the paper; do not re-fetch from arXiv.
- `codebase.zip` - snapshot of the official repo `github.com/E... | [
"Read `paper.pdf` and identify Table 2 as the correlation table (R² and Pearson between ID LCA/Top1 and OOD Top1/Top5 across five ImageNet-OOD datasets and 75 pretrained models).",
"Unpack `input/codebase.zip` and use the precomputed `ICML_dict_result_metric_dict` + `ICML_dict_agreement_dict_*.npy` artifacts to r... | [
"Python",
"PyTorch",
"NumPy",
"pandas",
"SciPy",
"scikit-learn",
"statsmodels"
] | [
{
"name": "Paper PDF",
"format": "pdf",
"path": "input/paper.pdf",
"description": "ICML 2024 paper \"LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies\" (arXiv:2407.16067v1). Local copy."
},
{
"name": "Codebase snapshot",
"format": "zip",
"path": ... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.3",
"subdomain_code": "ai_cs_research",
"subdomain_name": "AI Engineering, Safety & CS Research"
} | tasks/computing_math/paper_reproduction_instance_1 |
computing_math/particle_filter_nonlinear_tracking | Particle Filter Nonlinear Tracking | Implement a three-tier particle-filter benchmark in Python, covering a linear-Gaussian validation case, 2D range-bearing tracking, and a coordinated-turn smoother case with Student-t process noise. | computing_math | Mathematical & Operations Research | last-exam | You are working on an Ubuntu coding task about particle filtering for nonlinear tracking.
Task directory:
- `base`
Visible input files:
- Problem specification: `base/input/problem_spec.md`
- Python runtime manifest: `base/input/runtime_env/pyproject.toml`
- Python lockfile: `base/input/runtime_env/uv.lock`
- Canonic... | [
"Read the staged problem specification in full before implementing the solver.",
"Use the staged Python wrapper if a task-local NumPy/SciPy runtime is needed.",
"Implement the solver in `output/pf_solver.py`.",
"Produce `tier1_results.npz`, `tier2_results.npz`, `tier3_results.npz`, and `results.json` under th... | [
"Python",
"NumPy",
"SciPy",
"uv"
] | [
{
"name": "problem_spec.md",
"format": "Markdown",
"path": "input/problem_spec.md",
"description": "Full public task specification for the three-tier particle-filter benchmark."
},
{
"name": "runtime_env/pyproject.toml",
"format": "TOML",
"path": "input/runtime_env/pyproject.toml",
... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.4",
"subdomain_code": "math_ops_research",
"subdomain_name": "Mathematical & Operations Research"
} | tasks/computing_math/particle_filter_nonlinear_tracking |
computing_math/pcap_enterprise_triage_01 | Enterprise PCAP Triage In Wireshark | Analyze a staged enterprise packet capture in Wireshark, identify the compromised internal host, reconstruct the infection chain, and deliver a structured triage report. | computing_math | Cybersecurity & Digital Forensics | last-exam | You are a cybersecurity analyst working on a Windows VM.
## Your Task
Use Wireshark to analyze the staged enterprise packet capture and produce a structured triage report.
## Input Files
- PCAP: `base\input\capture_enhanced.pcap`
- Output schema: `base\input\output_schema.json`
- Software notes: `base\software\README... | [
"Open the staged packet capture in Wireshark on the Windows VM.",
"Identify the compromised internal workstation from the network evidence.",
"Reconstruct the infection timeline in chronological order with the required timestamps and endpoints.",
"Recover the initial vector, malicious delivery URL, and C2 ser... | [
"Wireshark"
] | [
{
"name": "Enterprise packet capture",
"format": "PCAP",
"path": "input/capture_enhanced.pcap",
"description": "The staged network capture the agent must inspect in Wireshark."
},
{
"name": "Visible output schema",
"format": "JSON",
"path": "input/output_schema.json",
"descriptio... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.5",
"subdomain_code": "cybersecurity",
"subdomain_name": "Cybersecurity & Digital Forensics"
} | tasks/computing_math/pcap_enterprise_triage_01 |
computing_math/ranking_node_feature_parity_recovery_instance_1 | Ranking Node Feature Parity Recovery | Recover a ranking-service node by rebuilding the feature manifest, deleting only safe rollback debris, and preserving required serving shards. | computing_math | Software Engineering | full-spectrum | You are the on-call ML platform engineer for a Linux ranking-serving node.
The evaluator will prepare a writable task workspace at:
- `/workspace`
Start by reading:
- `/workspace/instruction.md`
- `/workspace/config.json`
- `/workspace/logs/service.log`
You must repair the node by implementing:
- `/workspace/safe_re... | [] | [
"Python 3",
"pytest"
] | [] | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.1",
"subdomain_code": "software_eng",
"subdomain_name": "Software Engineering"
} | tasks/computing_math/ranking_node_feature_parity_recovery_instance_1 |
computing_math/recsys_cold_start_instance_1 | Hybrid Recommender Cold-Start Benchmark | Build a warm-plus-cold hybrid recommender on Linux, using temporal splitting, metadata/embedding cold-start ranking, and a combined output contract. | computing_math | Data & Analytics Engineering | near-term | You are building a hybrid recommender system on a Linux VM.
Visible inputs:
- `base/input/interactions.csv`
- `base/input/item_metadata.csv`
- `base/input/item_embeddings.json`
- `base/input/user_profiles.csv`
- `base/input/runtime_env/pyproject.toml`
- `base/input/runtime_env/uv.lock`
- `base/software/setup_runtime_e... | [
"Build a warm-item recommender that uses both `rating` and `watch_percentage`.",
"Build a cold-start model from `item_metadata.csv` and `item_embeddings.json` without using cold-item interactions.",
"Use the canonical per-user temporal split: `70%` train, `10%` validation, `20%` test.",
"Fuse warm and cold sc... | [
"Python 3.10",
"uv",
"numpy",
"pandas",
"scikit-learn",
"scipy"
] | [
{
"name": "interactions.csv",
"format": "csv",
"path": "input/interactions.csv",
"description": "Warm-item user interaction history with rating, watch percentage, and timestamps."
},
{
"name": "item_metadata.csv",
"format": "csv",
"path": "input/item_metadata.csv",
"description":... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.2",
"subdomain_code": "data_analytics",
"subdomain_name": "Data & Analytics Engineering"
} | tasks/computing_math/recsys_cold_start_instance_1 |
computing_math/synthetic_causal_structure_inference | Synthetic Causal Structure Inference | Inspect 40 synthetic tabular datasets, infer each dataset's causal scenario and graph structure, and submit one combined JSON benchmark prediction file. | computing_math | Data & Analytics Engineering | near-term | You are working on a Linux VM to solve a synthetic causal-structure benchmark.
## Input Files
- Task brief: `base/input/task_brief.md`
- Benchmark prompt: `base/input/AGENT_PROMPT_FOR_SYNTHETIC_CAUSAL_TASK.md`
- Sanitized dataset manifest: `base/input/manifest.json`
- Dataset directories: `base/input/public/dataset_##... | [
"Read the staged task brief, benchmark prompt, and sanitized manifest.",
"Inspect each dataset directory under `input/public/dataset_###/`.",
"Infer the scenario family, identification strategy, identifiability, variable roles, directed edges, and latent confounders for every dataset.",
"Write one final `subm... | [
"Python",
"pandas",
"numpy",
"statsmodels",
"scikit-learn",
"networkx"
] | [
{
"name": "Task brief",
"format": "`.md`",
"path": "input/task_brief.md",
"description": "High-level wrapper with output path and runtime notes"
},
{
"name": "Benchmark prompt",
"format": "`.md`",
"path": "input/AGENT_PROMPT_FOR_SYNTHETIC_CAUSAL_TASK.md",
"description": "Detailed... | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.2",
"subdomain_code": "data_analytics",
"subdomain_name": "Data & Analytics Engineering"
} | tasks/computing_math/synthetic_causal_structure_inference |
computing_math/tris_crackme | Tris Crackme | Analyze the bundled registration crackme and make the program appear successfully registered. | computing_math | Cybersecurity & Digital Forensics | full-spectrum | Goal:
Solve the registration crackme and make the bundled Windows program appear registered.
Target program:
- Executable: base\input\tris.exe
Requirements:
1. Launch and analyze the program if needed.
2. Make the registration succeed. You can use any method to achieve this, such as reverse engineering, patching, or ... | [
"Reverse the bundled registration crackme.",
"Make the program appear registered, typically by writing the correct registry values.",
"Leave the registered state persistent across restarts."
] | [
"Python"
] | [
{
"name": "tris.exe",
"format": "Windows PE",
"path": "input/tris.exe",
"description": "agent-visible target"
}
] | {
"domain_id": "9",
"domain_code": "computing_math",
"subdomain_id": "9.5",
"subdomain_code": "cybersecurity",
"subdomain_name": "Cybersecurity & Digital Forensics"
} | tasks/computing_math/tris_crackme |
education_info/homework_grading_numerical_pdes_instance_02 | Homework Grading Numerical PDEs 02 | Grade five synthetic graduate numerical-PDE homework submissions from released materials, then emit scores, error tags, per-student feedback, a class summary, and a grader manifest. | education_info | Educational Technology | near-term | You are grading synthetic graduate numerical-PDE homework submissions on Linux.
Task directory:
- `base`
Visible inputs:
- Instructions: `base/input/TASK_PROMPT.md`
- Released materials: `base/input/released/`
- Starter scaffold: `base/input/starter_project/`
Your task:
1. Read the grading protocol, rubric, solution... | [
"Read the released grading materials and student submissions.",
"Produce rubric-aligned grades for all five students.",
"Assign the appropriate error tags.",
"Write all five required deliverables under `output/`."
] | [
"Python"
] | [
{
"name": "Released materials",
"format": "Markdown, PDF, JSON",
"path": "input/released/",
"description": "Problem set, rubric, solution key, protocol, and student submissions."
},
{
"name": "Starter scaffold",
"format": "Python + JSON",
"path": "input/starter_project/",
"descri... | {
"domain_id": "11",
"domain_code": "education_info",
"subdomain_id": "11.1",
"subdomain_code": "edtech",
"subdomain_name": "Educational Technology"
} | tasks/education_info/homework_grading_numerical_pdes_instance_02 |
education_info/marc_remediation_folio_overlay | MARC Remediation FOLIO Overlay | Complete a Python starter project that remediates legacy MARC batches, applies cataloging policy rules, and prepares deterministic FOLIO Data Import overlay artifacts. | education_info | Library & Information Science | full-spectrum | You are working on a Linux VM to complete a MARC remediation and FOLIO overlay starter project.
## Task Directory
`base`
## Visible Inputs
- Full prompt: `base/input/TASK_PROMPT.md`
- Harness summary: `base/input/task_spec.md`
- Editable starter project: `base/input/starter_project`
- Visible public case: `base/input... | [
"Copy `input/starter_project/` to `output/submission/` before editing.",
"Preserve the CLI contract: `python scripts/remediate_catalog.py --case-dir <case_dir> --output-dir <output_dir>`.",
"Apply the visible cataloging policy uniformly to MARCXML/MRK records, FOLIO matches, authority mappings, and 949 holdings... | [
"Python"
] | [
{
"name": "TASK_PROMPT.md",
"format": "Markdown",
"path": "input/TASK_PROMPT.md",
"description": "Full agent-facing task prompt."
},
{
"name": "task_spec.md",
"format": "Markdown",
"path": "input/task_spec.md",
"description": "Harness-facing summary of the expected starter-copy w... | {
"domain_id": "11",
"domain_code": "education_info",
"subdomain_id": "11.2",
"subdomain_code": "library_info",
"subdomain_name": "Library & Information Science"
} | tasks/education_info/marc_remediation_folio_overlay |
education_info/moodle_gradebook_closeout_reconciliation | Moodle Gradebook Closeout Reconciliation | Repair a broken offline Moodle-style course backup and rebuild the registrar plus OneRoster closeout exports from the repaired bundle. | education_info | Educational Technology | full-spectrum | You are working on Linux on an offline Moodle gradebook closeout task.
## Task Root
- `base`
## Visible Inputs
- Task prompt: `base/input/TASK_PROMPT.md`
- Bundle README: `base/input/README.md`
- Shared helper library: `base/input/bundle_lib.py`
- Starter project: `base/input/starter_project`
- Starter backup: `base/... | [
"Read `input/TASK_PROMPT.md` and the starter materials under `input/starter_project/`.",
"Repair the offline Moodle-style backup `input/starter_project/course_backup.mbz` by editing only the benchmark-designated editable backup files.",
"Rebuild the registrar export and the OneRoster package with the staged hel... | [
"Python",
"pandas",
"uv"
] | [
{
"name": "TASK_PROMPT.md",
"format": "Markdown",
"path": "input/TASK_PROMPT.md",
"description": "Agent-visible benchmark contract for the offline Moodle gradebook closeout workflow."
},
{
"name": "README.md",
"format": "Markdown",
"path": "input/README.md",
"description": "Top-l... | {
"domain_id": "11",
"domain_code": "education_info",
"subdomain_id": "11.1",
"subdomain_code": "edtech",
"subdomain_name": "Educational Technology"
} | tasks/education_info/moodle_gradebook_closeout_reconciliation |
education_info/yi_manuscript_translation_1 | Yi Manuscript Translation 1 | Inspect a classical Yi manuscript image, identify which glyph a phonetic gloss refers to, localize that glyph, and write a cited translation report from bundled source texts. | education_info | Translation & Localization | last-exam | You are analyzing a classical Yi manuscript image on a Windows VM.
## Task Folder
`base`
## Visible Input Files
- Question: `base\input\question.txt`
- Manuscript image: `base\input\inscription.png`
- Bundled source texts: `base\input\reference_materials\`
## Software
- Image launcher: `base\software\launch_viewer.c... | [
"Read `input/question.txt` and inspect the staged `input/inscription.png` image via `software/launch_viewer.cmd`.",
"Determine which glyph the phonetic gloss refers to by using only the bundled `input/reference_materials/` files.",
"Write `output/bounding_box.json` with integer pixel coordinates for the target ... | [
"Windows Image Viewer",
"Text Editor"
] | [
{
"name": "Task brief",
"format": "`.txt`",
"path": "input/question.txt",
"description": "Explains the manuscript-inspection and reporting task."
},
{
"name": "Manuscript image",
"format": "`.png`",
"path": "input/inscription.png",
"description": "Classical Yi manuscript image co... | {
"domain_id": "11",
"domain_code": "education_info",
"subdomain_id": "11.3",
"subdomain_code": "translation",
"subdomain_name": "Translation & Localization"
} | tasks/education_info/yi_manuscript_translation_1 |
engineering/2d_drawings_to_3d_bridge_model | Bridge The Gap — Bridge + Site 3D Model | Extend an existing 3D site model (terrain + surrounding buildings) by adding a bridge designed in the supplied posters. Output must contain BOTH the unchanged site AND the new bridge geometry. The model is exported as model.obj plus model.3dm; an image-only multimodal LLM judge scores 12 rendered views (4 elevations + ... | engineering | Civil, Architectural & Geospatial Engineering | null | You are a BIM modeler using Rhino 8 to add a bridge design to an existing 3D site model.
This is NOT a build-from-scratch task. The site (terrain + surrounding buildings) already exists — you must place the bridge into it and preserve the rest of the site unchanged.
## Inputs
All input materials are under:
E:\agent... | [
"Read input/README.md and input/output_contract.json to understand the brief, the required filenames, and the coordinate convention.",
"Open Rhino 8 via the staged software/Rhino 8.lnk shortcut.",
"Open input/site_model/3D Model_Site.3dm in Rhino to inherit the world coordinate frame and the existing terrain + ... | [
"Rhino 8"
] | [
{
"name": "README.md",
"format": "Markdown",
"path": "input/README.md",
"description": "Natural-language task brief that mirrors the prompt and lists the required bridge components."
},
{
"name": "output_contract.json",
"format": "JSON",
"path": "input/output_contract.json",
"des... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.3",
"subdomain_code": "civil_geo",
"subdomain_name": "Civil, Architectural & Geospatial Engineering"
} | tasks/engineering/2d_drawings_to_3d_bridge_model |
engineering/2d_drawings_to_3d_building_model | Betonwerk Katzenberger 3D Model | Build a 3D architectural model of the Betonwerk Katzenberger building (Bavarian concrete plant — workshop Hall + residential Tower) in Rhino 8 by reading six PDF drawings, a 3D snapshot, and a footprint-only positioning anchor. Export as model.obj plus model.3dm; an image-only multimodal LLM judge scores 14 rendered vi... | engineering | Civil, Architectural & Geospatial Engineering | last-exam | You are a BIM modeler using Rhino 8 to build the Betonwerk Katzenberger building in 3D, given the architectural drawings of the project.
The building is a Bavarian concrete-plant complex with two main parts:
- a wide, low workshop Hall housing three prefabricated workshop modules
- a tall narrow residential Tower ... | [
"Read input/README.md and input/output_contract.json to understand the brief, the required filenames, and the coordinate convention.",
"Open Rhino 8 via the staged software/Rhino 8.lnk shortcut.",
"Open the 6 PDF drawings in input/architectural_drawings/ and the base_model.3dm to study form and lock the world c... | [
"Rhino 8"
] | [
{
"name": "README.md",
"format": "Markdown",
"path": "input/README.md",
"description": "Natural-language task brief that mirrors the prompt and lists the required components."
},
{
"name": "output_contract.json",
"format": "JSON",
"path": "input/output_contract.json",
"descriptio... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.3",
"subdomain_code": "civil_geo",
"subdomain_name": "Civil, Architectural & Geospatial Engineering"
} | tasks/engineering/2d_drawings_to_3d_building_model |
engineering/Analog_Active | Analog Active | Design and simulate the required analog circuit in LTspice from the staged specification and deliver the completed schematic output. | engineering | Electronics Engineering | full-spectrum | You are given an LTspice schematic with the LTM4648 µModule DC/DC buck regulator IC already placed on the canvas. Your task is to complete the circuit design by adding all required external components, wiring them to the correct IC pins, and running a transient simulation to verify the output.
... | [
"Open the staged LTspice starter schematic for the active variant.",
"Read the design specification and LTM4648 datasheet to choose the required external component values and wiring.",
"Complete the buck-converter schematic, add a `1 ms` startup transient analysis, save the updated `circuit.asc`, and run simula... | [
"Python"
] | [
{
"name": "design_spec.txt",
"format": "text",
"path": "input/design_spec.txt",
"description": "Active-variant targets for VIN, VOUT, load current, ripple, and startup behavior"
},
{
"name": "ltm4648_datasheet.pdf",
"format": "PDF",
"path": "input/ltm4648_datasheet.pdf",
"descrip... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.2",
"subdomain_code": "electronics",
"subdomain_name": "Electronics Engineering"
} | tasks/engineering/Analog_Active |
engineering/abb_irb6700_asset_to_urdf_instance_1 | ABB IRB6700 Asset To URDF | Reconstruct a complete ABB IRB6700 robot URDF from staged mesh assets and structural metadata. The agent must preserve the required links, joints, limits, mimic behavior, and auxiliary frames while producing exactly one valid `submission.urdf` file. | engineering | Robotics & Autonomous Systems | near-term | You are reconstructing a robot URDF on a Linux VM.
## Variant
`base`: ABB IRB6700 URDF reconstruction
## Input Files
- Mesh assets: `base/input/meshes`
- Structural metadata: `base/input/metadata`
- Task brief: `base/input/task_brief.md`
## What You Must Do
1. Read `base/input/task_brief.md`.
2. Use the mesh assets ... | [
"1. Read the staged task brief and inspect the ABB IRB6700 meshes plus metadata.",
"2. Reconstruct a valid URDF with the required links, joints, limits, mimic behavior, and auxiliary frames.",
"3. Save exactly one final file named `submission.urdf` under `output/`."
] | [
"Python"
] | [
{
"name": "meshes/",
"format": "stl directory",
"path": "input/meshes/",
"description": "Visual and collision STL meshes for the ABB IRB6700 links."
},
{
"name": "link_manifest.json",
"format": "json",
"path": "input/metadata/link_manifest.json",
"description": "Lists the require... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.11",
"subdomain_code": "robotics",
"subdomain_name": "Robotics & Autonomous Systems"
} | tasks/engineering/abb_irb6700_asset_to_urdf_instance_1 |
engineering/aerospace_low_thrust_trajectory | Low-Thrust LEO To GEO Trajectory | Compute a three-tier orbital transfer from inclined low Earth orbit to geostationary orbit using analytic, numerical low-thrust, and optimal-control methods. | engineering | Aerospace & Mechanical Engineering | last-exam | You are working on a Linux VM.
Task root:
- `base`
Visible files:
- Problem specification: `base/input/problem_spec.md`
- Task prompt: `base/input/task_prompt.md`
- Output contract: `base/input/output_contract.json`
- Python runtime manifest: `base/input/runtime_env/pyproject.toml`
- Task-scoped Python entry point: `... | [
"1. Compute the Tier 1 Hohmann transfer quantities.",
"2. Integrate the Tier 2 continuous tangential low-thrust spiral and save the required trajectory array.",
"3. Solve the Tier 3 fixed-time low-thrust transfer with inclination change and save the required trajectory and control arrays.",
"4. Write `output/... | [
"Python",
"uv",
"NumPy",
"SciPy"
] | [
{
"name": "problem_spec.md",
"format": "md",
"path": "input/problem_spec.md",
"description": "Full low-thrust LEO-to-GEO problem statement with formulas, state definitions, and output requirements."
},
{
"name": "task_prompt.md",
"format": "md",
"path": "input/task_prompt.md",
"d... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.1",
"subdomain_code": "aerospace_mech",
"subdomain_name": "Aerospace & Mechanical Engineering"
} | tasks/engineering/aerospace_low_thrust_trajectory |
engineering/cailian_road_highway_alignment_2 | Cailian Road Highway Alignment | Use AutoCAD Civil 3D 2024 to design a Cailian Road horizontal alignment, generate an existing-ground surface profile, and export station-sampled alignment metrics. | engineering | Civil, Architectural & Geospatial Engineering | full-spectrum | You are a civil engineer using AutoCAD Civil 3D 2024 on Windows.
## Your Task
Design a horizontal alignment for Cailian Road and generate a vertical profile that follows the existing ground surface.
## Control Points
- Start: X = -52093.6660, Y = -5836.2683, Z = 5.5
- End: X = -50855.6202, Y = -4142.4687, Z = 5.3
-... | [
"Launch AutoCAD Civil 3D 2024 through the staged task-local launcher.",
"Open the staged `input/topo_surface.dwg` existing-ground TIN surface.",
"Create a non-trivial Cailian Road horizontal alignment that connects the fixed control points and satisfies the stated curve, spiral, speed, and total-length constrai... | [
"Autodesk Civil 3D 2024"
] | [
{
"name": "topo_surface.dwg",
"format": "Autodesk DWG",
"path": "input/topo_surface.dwg",
"description": "Existing-ground TIN surface for the Cailian Road corridor."
},
{
"name": "open_civil3d_2024.bat",
"format": "Windows batch script",
"path": "software/open_civil3d_2024.bat",
... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.3",
"subdomain_code": "civil_geo",
"subdomain_name": "Civil, Architectural & Geospatial Engineering"
} | tasks/engineering/cailian_road_highway_alignment_2 |
engineering/chisel_verilog_alignment_seq_1 | Chisel-to-Verilog Source Location Alignment | Given a Chisel hardware design (Fifo.scala) and its firtool-optimized SystemVerilog output, identify the exact set of Chisel source locations that a specific sub-expression semantically corresponds to. | engineering | Semiconductor & Microelectronics Design | full-spectrum | You are a hardware design engineer specializing in Chisel and SystemVerilog.
## Your Task
Given a Chisel hardware design (Fifo.scala) and its firtool-optimized SystemVerilog output, identify the exact set of Chisel source locations (Fifo.scala:line:col) that a specific sub-expression of the optimized Verilog semantic... | [
"Read TASK.md for the detailed query",
"Examine Fifo.scala and optimized.sv",
"Perform genuine semantic analysis (not just read annotations)",
"Write output/answer.json with the correct chisel_sources set"
] | [
"Yosys 0.33+",
"firtool 1.138.0 (CIRCT)",
"sbt 1.9+ with Chisel 6.5.0",
"Python 3.10"
] | [
{
"name": "Task instructions",
"format": "md",
"path": "input/workdir/TASK.md",
"description": "Restates the query and includes firtool annotation noisiness disclaimer"
},
{
"name": "Chisel source",
"format": "scala",
"path": "input/workdir/src/main/scala/Fifo.scala",
"descriptio... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.8",
"subdomain_code": "semiconductor",
"subdomain_name": "Semiconductor & Microelectronics Design"
} | tasks/engineering/chisel_verilog_alignment_seq_1 |
engineering/gcode | gcode | Create machining toolpaths for the staged workpiece in PowerMill and deliver the required NC output. | engineering | Manufacturing & Industrial Systems | last-exam | You are a CAM (Computer-Aided Manufacturing) engineer using Autodesk PowerMill.
## Your Task
Design toolpaths to machine workpiece **125162_319** using the provided blank PowerMill project.
## Input Files
- **PowerMill project** (blank, with pre-configured tools, zero toolpaths):
`125162_319\input\125162-319-NCFM-T... | [
"Open the staged blank PowerMill project for the selected workpiece.",
"Review the staged `.prt` geometry and `.jpg` visual reference.",
"Design roughing and finishing toolpaths using the preconfigured tool library.",
"Ensure the toolpaths have no collisions and no gouges.",
"Run a stock-model simulation in... | [
"Python"
] | [
{
"name": "Blank PowerMill project",
"format": "directory",
"path": "input/<PM_PROJECT_NAME>/",
"description": "cloned from the expert PM project with all toolpaths and stock models removed"
},
{
"name": "Ideal workpiece geometry",
"format": "`.prt`",
"path": "input/<variant_name>.pr... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.5",
"subdomain_code": "manufacturing",
"subdomain_name": "Manufacturing & Industrial Systems"
} | tasks/engineering/gcode |
engineering/humanoid_velocity_tracking_policy | Humanoid Velocity Tracking Policy | Produce a deterministic Isaac Lab humanoid locomotion policy that tracks commanded planar velocities and satisfies fixed API, stability, and rollout metric gates. | engineering | Robotics & Autonomous Systems | last-exam | You are producing a policy submission for an Isaac Lab humanoid velocity-tracking benchmark.
## Variant
`base`: Humanoid velocity tracking policy
## Input Files
- Task brief: `base\input\task_brief.md`
- Success criteria and exact evaluation command: `base\input\SUCCESS.md`
- Observation/action ordering: `base\input\... | [
"Read the staged task brief, proprioceptive-state spec, and success criteria.",
"Produce a deterministic `Policy` implementation that maps `(command, state)` tensors to 23 humanoid joint actions.",
"Save exactly `policy.py` and `checkpoint.pt` in the runtime output directory."
] | [
"Isaac Lab",
"Isaac Sim",
"PyTorch",
"CUDA"
] | [
{
"name": "task_brief.md",
"format": "md",
"path": "input/task_brief.md",
"description": "Agent-facing summary of the policy submission task."
},
{
"name": "SUCCESS.md",
"format": "md",
"path": "input/SUCCESS.md",
"description": "Hard gates, reference evaluation command, metrics,... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.11",
"subdomain_code": "robotics",
"subdomain_name": "Robotics & Autonomous Systems"
} | tasks/engineering/humanoid_velocity_tracking_policy |
engineering/humanoid_wbc_policy_evaluation | null | Run mjlab whole-body-control rollouts for 8 Unitree G1 humanoid policy cases, classify each as successful/nearly_successful/failed, and emit a schema-conformant `policy_evaluation_report.json` with per-case visual demo artifacts. | engineering | Robotics & Autonomous Systems | near-term | You are evaluating whole-body-control policy rollouts for a Unitree G1 humanoid in mjlab.
## Variant
``:
## Input Files
- Task brief: `/input/task_brief.md`
- Policy case list: `/input/policy_cases.json`
- Required JSON output schema: `/input/output_schema.json`
- mjlab runtime archive and exported package list: `/i... | [] | [
"mjlab",
"MuJoCo",
"PyTorch",
"wandb"
] | [] | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.11",
"subdomain_code": "robotics",
"subdomain_name": "Robotics & Autonomous Systems"
} | tasks/engineering/humanoid_wbc_policy_evaluation |
engineering/inner_support_elevation_optimization | Inner Support Elevation Optimization | Use PLAXIS 3D to compare five inner support elevations for a staged pit-in-pit excavation and report settlement and lateral displacement outcomes with the required engineering deliverables. | engineering | Civil, Architectural & Geospatial Engineering | last-exam | You are a geotechnical engineer using PLAXIS 3D 2023.2 on Windows.
## Your Task
Evaluate how the inner support elevation changes settlement and lateral wall movement for the staged A-zone pit-in-pit excavation benchmark.
## Input Files
- Benchmark model specification: `base\input\benchmark_model_spec_en.md`
- Enginee... | [
"Review the staged benchmark specification, engineering assumptions, figures, and answer template.",
"Build or duplicate the five PLAXIS 3D support-elevation cases for `0 m`, `1 m`, `2 m`, `3 m`, and `4 m`.",
"Run the calculations through the final excavation state and extract maximum settlement plus maximum ou... | [
"PLAXIS 3D 2023.2"
] | [
{
"name": "benchmark_model_spec_en.md",
"format": "md",
"path": "input/benchmark_model_spec_en.md",
"description": "Full staged excavation benchmark specification for the A-zone pit-in-pit model."
},
{
"name": "engineering_assumptions_en.md",
"format": "md",
"path": "input/engineerin... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.3",
"subdomain_code": "civil_geo",
"subdomain_name": "Civil, Architectural & Geospatial Engineering"
} | tasks/engineering/inner_support_elevation_optimization |
engineering/kicad_navswitch_library_integration_release_002 | KiCad Navswitch Library Integration | Repair a SparkFun Qwiic Navigation Switch KiCad project by restoring the local symbol and footprint integration for U4 and producing release artifacts. | engineering | Electronics Engineering | near-term | You are repairing a KiCad project for the SparkFun Qwiic Navigation Switch board.
## Input
- Broken KiCad project: `input/project/SparkFun_Qwiic_Navigation.kicad_pro`
- Project files and local libraries: `input/project`
- Datasheets: `input/datasheets`
- KiCad launcher: `software/OpenKiCad.bat`
## Your Task
1. Open t... | [
"Use KiCad UI to inspect and repair the project.",
"Restore U4's footprint assignment to the correct project-local footprint.",
"Update the PCB from the schematic and preserve unrelated design intent.",
"Export the required project, library, ERC/DRC, netlist, Gerber, drill, XML, and D356 deliverables."
] | [
"KiCad",
"Python"
] | [
{
"name": "SparkFun_Qwiic_Navigation project",
"format": "KiCad project",
"path": "input/project/",
"description": "Broken KiCad project and local libraries."
},
{
"name": "datasheets",
"format": "directory",
"path": "input/datasheets/",
"description": "Reference datasheets for t... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.2",
"subdomain_code": "electronics",
"subdomain_name": "Electronics Engineering"
} | tasks/engineering/kicad_navswitch_library_integration_release_002 |
engineering/mold-flow | mold-flow | Configure process parameters in Moldex3D, run the staged mold-flow analysis, and save the required outputs. | engineering | Manufacturing & Industrial Systems | last-exam | You are a CAE (Computer-Aided Engineering) engineer performing injection molding simulation using Moldex3D.
## Your Task
Configure process parameters and run a mold-flow analysis for project **230057** using the Moldex3D software.
## Input Files
- **Moldex3D project** (mesh + material already configured, NO process p... | [
"Open the staged Moldex3D project for the selected variant.",
"Read `input/process_spec.json` and configure the process parameters inside Moldex3D.",
"Run the Fill → Pack → Cool (Transient) → Warp analysis sequence.",
"Extract the required simulation values after the run completes.",
"Fill `output/results.j... | [
"Python"
] | [
{
"name": "Clean Moldex3D project",
"format": "directory",
"path": "input/<PROJECT_NAME>/",
"description": "mesh/material/source present; answer-leaking .sum and .pro files removed"
},
{
"name": "Part CAD geometry (optional)",
"format": "`.x_t`",
"path": "input/<variant_name>.x_t",
... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.5",
"subdomain_code": "manufacturing",
"subdomain_name": "Manufacturing & Industrial Systems"
} | tasks/engineering/mold-flow |
engineering/mpc_control_building_v1 | MPC Control In Building | Develop and evaluate baseline, energy-saving MPC, and demand-response MPC cooling control for a single-family EnergyPlus house model. | engineering | Energy, Power & Nuclear Engineering | full-spectrum | You are a building-controls engineer developing model predictive control (MPC)
for a single-family house cooling system.
## Input
Use the task input directory `base/input`. It contains:
- `SFH.idf`: the EnergyPlus 22.1.0 building model.
- `Denver_current_TMY.epw`: the weather file.
- `task_spec.json`: deterministic ... | [] | [
"EnergyPlus 22.1.0",
"Python",
"uv",
"pandas",
"numpy",
"scipy",
"cvxpy"
] | [] | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.6",
"subdomain_code": "energy_power",
"subdomain_name": "Energy, Power & Nuclear Engineering"
} | tasks/engineering/mpc_control_building_v1 |
engineering/openroad_sky130_ibex_pnr_signoff | OpenROAD SKY130 Ibex PnR Signoff | Tune the lowRISC Ibex SKY130HD OpenROAD-flow-scripts configuration, rerun the pinned Docker flow, and submit the final config plus pass-audit evidence needed for signoff review. | engineering | Semiconductor & Microelectronics Design | last-exam | You are working on Linux on an RTL-to-GDSII signoff-closure task for lowRISC ibex on sky130hd.
## Task Root
- `base`
## Visible Inputs
- Task brief: `base/input/task_instructions.md`
- Starter tree: `base/input/starter_project`
- Workspace helper: `base/software/prepare_workspace.sh`
## What You Should Do
1. Read `b... | [
"Read the staged task brief and create a writable workspace with `software/prepare_workspace.sh`.",
"Edit only `output/workspace/flow/designs/sky130hd/ibex/config.mk` and `output/workspace/JOURNAL.md`.",
"Run the pinned OpenROAD-flow-scripts Docker flow from the writable workspace.",
"Copy `config.mk`, `JOURN... | [
"OpenROAD-flow-scripts",
"Docker",
"Python"
] | [
{
"name": "task_instructions.md",
"format": "Markdown",
"path": "input/task_instructions.md",
"description": "Staged task brief defining the allowed edits, pass budget, and required handoff files."
},
{
"name": "starter_project",
"format": "directory",
"path": "input/starter_project"... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.8",
"subdomain_code": "semiconductor",
"subdomain_name": "Semiconductor & Microelectronics Design"
} | tasks/engineering/openroad_sky130_ibex_pnr_signoff |
engineering/pcb_layout_kicad_1 | Mini Encabulator KiCad PCB Layout | Create a routed KiCad PCB layout from the staged Mini Encabulator schematic. | engineering | Electronics Engineering | last-exam | You are designing a KiCad PCB layout for a Mini Encabulator.
## Input
- Schematic: `base\input\mini_encabulator.kicad_sch`
- KiCad launcher: `base\software\OpenKiCad.bat`
## Your Task
1. Open the schematic in KiCad and create a PCB layout from it.
2. Import all assigned footprints from the schematic.
3. Place all com... | [
"Open the staged Mini Encabulator schematic in KiCad.",
"Create and complete a PCB layout from the assigned footprints.",
"Add the required board outline, mounting holes, routed traces, GND pours, and stitching vias.",
"Run DRC and save the final .kicad_pcb output."
] | [
"KiCad"
] | [
{
"name": "mini_encabulator.kicad_sch",
"format": "KiCad schematic",
"path": "input/mini_encabulator.kicad_sch",
"description": "Agent-visible schematic with assigned footprints"
}
] | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.2",
"subdomain_code": "electronics",
"subdomain_name": "Electronics Engineering"
} | tasks/engineering/pcb_layout_kicad_1 |
engineering/power_10kv_feeder_reliability_001 | 10kV Feeder Reliability Indices | Compute IEEE/IEC supply reliability indices for a 10kV distribution feeder from staged CIM/RDF XML, SVG topology, and parameter data, then write the structured JSON result to the canonical output path. | engineering | Energy, Power & Nuclear Engineering | full-spectrum | You are computing IEEE/IEC supply reliability indices for a 10kV distribution feeder.
## Your Task
Read the staged feeder model, the SVG topology drawing, and the reliability parameters.
Compute the feeder reliability indices and the section-level contribution tables.
## Visible Inputs
- CIM/RDF XML: `base/input/gis.... | [
"Parse the staged CIM/RDF XML, SVG topology, and parameter JSON.",
"Build a section-level feeder model and compute the required reliability indices.",
"Write the structured result to `output/reliability_indices.json`.",
"Preserve the staged input and evaluator-only reference data."
] | [
"Python 3.12",
"pandas",
"openpyxl"
] | [
{
"name": "gis.null.xml",
"format": "XML",
"path": "input/gis.null.xml",
"description": "CIM/RDF feeder model."
},
{
"name": "gis.null.svg",
"format": "SVG",
"path": "input/gis.null.svg",
"description": "Single-line topology drawing."
},
{
"name": "params.json",
"form... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.6",
"subdomain_code": "energy_power",
"subdomain_name": "Energy, Power & Nuclear Engineering"
} | tasks/engineering/power_10kv_feeder_reliability_001 |
engineering/robotics_blender_tabletop_reconstruction | Blender Tabletop Workspace Reconstruction | Reconstruct a tabletop robotic-manipulation workspace in Blender from a floor plan, a reference render, and staged mesh assets, then save the scene, render a verification image, and export the required OBJ files. | engineering | 3D, Animation & Interactive Media | last-exam | You are a robotics-focused 3D scene artist using Blender.
Your task is to reconstruct a tabletop robotic-manipulation workspace from staged assets.
Agent-visible inputs:
- Export contract: `base\input\export_spec.json`
- Floor plan: `base\input\floor_plan.png`
- Reference render: `base\input\reference_render.png`
- M... | [
"Reconstruct the tabletop manipulation scene in Blender using the floor plan, reference render, and staged mesh assets.",
"Place the table and three YCB objects at the positions specified by the visible floor plan.",
"Infer rotations, materials, and lighting from the reference render.",
"Save `scene.blend`, r... | [
"Blender"
] | [
{
"name": "export_spec.json",
"format": "JSON",
"path": "input/export_spec.json",
"description": "Defines the required render and export filenames."
},
{
"name": "floor_plan.png",
"format": "PNG image",
"path": "input/floor_plan.png",
"description": "Provides explicit spatial val... | {
"domain_id": "8",
"domain_code": "visual_media",
"subdomain_id": "8.1",
"subdomain_code": "animation_3d",
"subdomain_name": "3D, Animation & Interactive Media"
} | tasks/engineering/robotics_blender_tabletop_reconstruction |
engineering/sanding_performance_scoring_instance_1 | Sanding Performance Image Scoring | Compute pixel-level paint-removal scores from reference and sanded panel images, then write a structured CSV with quantity, uniformity, and composite metrics. | engineering | Manufacturing & Industrial Systems | near-term | You are completing a sanding-performance image scoring task on a Windows VM.
Input files are staged in:
`base\input`
Read `algorithm_spec.md` in that directory. It defines how to use the reference images `panel_painted.png` and `panel_unpainted.png` to score:
- `panel_sanded_01.png`
- `panel_sanded_02.png`
- `panel_s... | [
"Read the visible algorithm specification and five panel images from the staged input directory.",
"Compute quantity, uniformity, and composite sanding scores for each of the three sanded panels.",
"Write `sanding_scores.csv` under the runtime output directory with the required schema and one row per sanded pan... | [
"Python"
] | [
{
"name": "algorithm_spec.md",
"format": "Markdown",
"path": "input/algorithm_spec.md",
"description": "Visible task algorithm and output schema."
},
{
"name": "panel_painted.png",
"format": "PNG image",
"path": "input/panel_painted.png",
"description": "Painted reference panel."... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.5",
"subdomain_code": "manufacturing",
"subdomain_name": "Manufacturing & Industrial Systems"
} | tasks/engineering/sanding_performance_scoring_instance_1 |
engineering/sumo_urban_am_peak_calibration | SUMO Urban AM Peak Calibration | Repair and calibrate a submitted SUMO 1.26 AM-peak microsimulation package for a synthetic downtown network, then export the required submission tree and calibration report. | engineering | Urban & Spatial Planning | full-spectrum | You are working on a Linux VM to build a calibrated SUMO AM-peak submission for an urban grid sub-network.
Visible task files:
- `base/input/task_prompt.md`
- `base/input/calibration_report.schema.json`
- `base/input/output_contract.json`
- `base/input/examples/report_template.example.json`
- `base/input/starter_proje... | [
"Read the staged task prompt and output contract before editing the submission tree.",
"Repair the starter SUMO configuration, demand, signal timing, and vehicle-type files inside the writable output tree.",
"Use the staged runtime wrappers if Python or SUMO commands are needed inside the task-local environment... | [
"SUMO",
"Python",
"uv",
"jsonschema",
"lxml"
] | [
{
"name": "task_prompt.md",
"format": "markdown",
"path": "input/task_prompt.md",
"description": "Agent-visible benchmark brief describing the visible contract and repair goals."
},
{
"name": "output_contract.json",
"format": "json",
"path": "input/output_contract.json",
"descrip... | {
"domain_id": "1",
"domain_code": "engineering",
"subdomain_id": "1.10",
"subdomain_code": "urban_planning",
"subdomain_name": "Urban & Spatial Planning"
} | tasks/engineering/sumo_urban_am_peak_calibration |
health_medicine/Clinical_Variant_Annotation | Clinical Variant Annotation | Analyze a staged clinical VCF, identify the pathogenic BRCA1-like candidate, and document the supporting evidence in the required output tables. | health_medicine | Genomics & Sequence Analysis | near-term | You are given a small clinical VCF containing 200 candidate variants from a breast-cancer case.
Your task is to identify the single pathogenic BRCA1-like candidate and document the evidence in the required output files.
Task directory:
- `base`
Input:
- `base/input/patient_variants.vcf`
Available tools:
- a Linux t... | [
"Read a small patient VCF, count variants, annotate candidates against public knowledge bases, and write a final pathogenic BRCA1-like candidate.",
"Write outputs only under `output/` while keeping committed fixtures untouched.",
"Work directly on Ubuntu; Windows + WSL is no longer the canonical runtime."
] | [] | [
{
"name": "patient_variants.vcf",
"format": "vcf",
"path": "input/patient_variants.vcf",
"description": ""
}
] | {
"domain_id": "3",
"domain_code": "life_sciences",
"subdomain_id": "3.2",
"subdomain_code": "genomics",
"subdomain_name": "Genomics & Sequence Analysis"
} | tasks/health_medicine/Clinical_Variant_Annotation |
health_medicine/causal_ihdp_ite_estimation_6a_v1 | IHDP ITE Estimation | Build a reusable hidden-test inference script for the IHDP semi-synthetic benchmark that estimates individualized treatment effects and emits the required diagnostics artifacts. | health_medicine | Economics & Quantitative Social Research | near-term | You are working on a Linux causal-inference benchmark.
Read these staged solve-time inputs first:
- `base/input/task_brief.md`
- `base/input/task_readme.md`
- `base/input/feature_schema.json`
- `base/input/train.csv`
- `base/input/dummy_test/test.csv`
If you want the pinned scientific Python stack, use:
- `base/softw... | [
"Read the staged IHDP training data, schema, benchmark brief, and task readme from `input/`.",
"Build a reusable individualized treatment-effect estimator over the 100 IHDP replications.",
"Author `output/predict.sh` so the evaluator can rerun hidden-test inference from the task root.",
"Write `output/output.... | [
"Python",
"uv",
"bash",
"numpy",
"pandas",
"scipy",
"scikit-learn"
] | [
{
"name": "task_brief.md",
"format": "Markdown",
"path": "input/task_brief.md",
"description": "Canonical agent-facing task brief with the exact `predict.sh` delivery contract."
},
{
"name": "task_readme.md",
"format": "Markdown",
"path": "input/task_readme.md",
"description": "N... | {
"domain_id": "13",
"domain_code": "social_sciences",
"subdomain_id": "13.1",
"subdomain_code": "economics",
"subdomain_name": "Economics & Quantitative Social Research"
} | tasks/health_medicine/causal_ihdp_ite_estimation_6a_v1 |
health_medicine/crf_sdtm_mapping_1 | CRF To SDTM Mapping Specification | Create a field-level mapping CSV from clinical CRF and define.xml source documents to SDTM domain variables. The task has CM, VS, and DS variants with matching supplemental qualifier datasets. | health_medicine | Clinical Research & Trial Operations | full-spectrum | You are preparing a field-level CRF-to-SDTM mapping specification on Linux.
## Your Task
Create the Concomitant Medications mapping for study C4591001.
## Visible Inputs
- Task brief: `base/input/task_brief.md`
- Output contract: `base/input/output_contract.json`
- Source documents directory: `base/input/source_docum... | [
"Read the variant's task brief and output contract.",
"Inspect the sample CRF, annotated CRF, SDTM define.xml, and supplemental define.xml source documents.",
"Identify CRF field-level mappings for the variant's primary SDTM dataset and supplemental qualifier dataset.",
"Write exactly one mapping CSV with the... | [
"Python",
"uv",
"pandas",
"pypdf",
"lxml"
] | [
{
"name": "task_brief.md",
"format": "md",
"path": "input/task_brief.md",
"description": "Variant-specific instructions and output filename."
},
{
"name": "output_contract.json",
"format": "json",
"path": "input/output_contract.json",
"description": "Required CSV columns, allowed... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.5",
"subdomain_code": "clinical_trials",
"subdomain_name": "Clinical Research & Trial Operations"
} | tasks/health_medicine/crf_sdtm_mapping_1 |
health_medicine/crf_sdtm_mapping_4 | CRF To SDTM Mapping Specification 4 | Create field-level mapping CSVs from clinical CRF and define.xml source documents to SDTM AE/SUPPAE or DM/SUPPDM variables. | health_medicine | Clinical Research & Trial Operations | near-term | You are preparing a field-level CRF-to-SDTM mapping specification on Linux.
## Your Task
Create the Adverse Events mapping for study C4591001.
## Visible Inputs
- Task brief: `base/input/task_brief.md`
- Output contract: `base/input/output_contract.json`
- Source documents directory: `base/input/source_documents`
- O... | [
"Read the variant's task brief and output contract.",
"Inspect the sample CRF, annotated CRF, SDTM define.xml, and supplemental define.xml source documents.",
"Identify CRF field-level mappings for the variant's primary SDTM dataset and supplemental qualifier dataset.",
"Write exactly one mapping CSV with the... | [
"Python",
"uv",
"pandas",
"pypdf",
"lxml"
] | [
{
"name": "task_brief.md",
"format": "md",
"path": "input/task_brief.md",
"description": "Variant-specific instructions and output filename."
},
{
"name": "output_contract.json",
"format": "json",
"path": "input/output_contract.json",
"description": "Required CSV columns, allowed... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.5",
"subdomain_code": "clinical_trials",
"subdomain_name": "Clinical Research & Trial Operations"
} | tasks/health_medicine/crf_sdtm_mapping_4 |
health_medicine/ct_geometry_calibration_catphan | CT Geometry Calibration (Catphan Phantom) | Calibrate CT geometry parameters (SAD, SDD, detector offset) for a Catphan phantom scan by iteratively optimizing a LEAP-based FBP reconstruction to match a reference image. | health_medicine | Clinical Diagnostics & Imaging | near-term | You are working on a Linux VM.
## Your Task
Calibrate the CT geometry parameters for a Catphan phantom reconstruction.
The input directory contains:
- `sinogram.npy` — measured sinogram (fan-beam CT, float32)
- `reference_image.npy` — ground-truth reconstructed image (512x512, float32)
- `reconstruct.py` — FBP recon... | [
"Inspect reconstruct.py to understand the LEAP-based FBP pipeline and nominal geometry parameters.",
"Run the initial reconstruction with nominal parameters and compare against reference_image.npy.",
"Iteratively optimize geometry parameters until SSIM >= 0.95 and unnormalized MSE <= 4e-6.",
"Save calibrated ... | [
"Python 3.10+",
"LEAP 1.26 (leaptorch)",
"NumPy",
"SciPy",
"scikit-image",
"PyTorch (CPU)"
] | [
{
"name": "Sinogram",
"format": "NumPy .npy (float32)",
"path": "input/sinogram.npy",
"description": "Measured sinogram of Catphan phantom (fan-beam CT)."
},
{
"name": "Reference image",
"format": "NumPy .npy (float32, 512x512)",
"path": "input/reference_image.npy",
"description"... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.1",
"subdomain_code": "clinical_diagnostics",
"subdomain_name": "Clinical Diagnostics & Imaging"
} | tasks/health_medicine/ct_geometry_calibration_catphan |
health_medicine/ecg_rhythm_conduction_ptbxl | ECG Rhythm Conduction Ptbxl | Variant ptbxl 2263: Single PTB XL derived rhythm/conduction benchmark slice with 2,263 anonymized ECG records. | health_medicine | Therapeutic & Oncology Services | near-term | You are a computational precision health researcher working on staged ECG waveform data.
## Variant
`ptbxl_2263`: Single PTB-XL-derived rhythm/conduction benchmark slice with 2,263 anonymized ECG records.
## Your Task
Build a multilabel classifier for rhythm and conduction findings from the staged WFDB ECG records.
... | [
"Open the staged task directory on the Windows VM and inspect the ECG bundle under `input/`.",
"Load all 2,263 WFDB records from `input/records/` and build a multi-label classifier for the 13 labels `SR`, `STACH`, `SBRAD`, `SARRH`, `AFIB`, `AFLT`, `PACE`, `CLBBB`, `CRBBB`, `LAFB`, `1AVB`, `IRBBB`, and `NORM`.",
... | [
"pandas",
"Python"
] | [
{
"name": "ECG waveform records",
"format": "WFDB `.hea` + `.dat` pairs",
"path": "input/records/",
"description": "2,263 anonymized 12-lead, 500 Hz, 10 s ECGs"
},
{
"name": "Record ID list",
"format": "`.csv`",
"path": "input/record_ids.csv",
"description": "One-column CSV with ... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.2",
"subdomain_code": "therapeutic_oncology",
"subdomain_name": "Therapeutic & Oncology Services"
} | tasks/health_medicine/ecg_rhythm_conduction_ptbxl |
health_medicine/ecg_superclass_ptbxl | ECG Superclass Ptbxl | Variant ptbxl 1386: Single PTB XL derived diagnostic superclass benchmark slice with 1,386 anonymized ECG records. | health_medicine | Therapeutic & Oncology Services | near-term | You are a computational precision health researcher working on staged ECG waveform data.
## Variant
`ptbxl_1386`: Single PTB-XL-derived diagnostic superclass benchmark slice with 1,386 anonymized ECG records.
## Your Task
Produce multilabel diagnostic superclass predictions for the staged WFDB ECG records.
## Input ... | [
"Open the staged task directory on the Windows VM and inspect the ECG bundle under `input/`.",
"Load all 1,386 WFDB records from `input/records/` and build a multi-label classifier for the five superclass labels `NORM`, `MI`, `STTC`, `CD`, and `HYP`.",
"Write `output/predictions.csv` with exactly the IDs from `... | [
"pandas",
"Python"
] | [
{
"name": "ECG waveform records",
"format": "WFDB `.hea` + `.dat` pairs",
"path": "input/records/",
"description": "1,386 anonymized 12-lead, 500 Hz, 10 s ECGs"
},
{
"name": "Record ID list",
"format": "`.csv`",
"path": "input/record_ids.csv",
"description": "Exact required outpu... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.2",
"subdomain_code": "therapeutic_oncology",
"subdomain_name": "Therapeutic & Oncology Services"
} | tasks/health_medicine/ecg_superclass_ptbxl |
health_medicine/epidemiology_forecast | Epidemiology Forecast | Reproduce the 2021-22 CDC FluSight influenza-hospitalization end-of-season scorecard and per-cell scoring table from staged forecast and truth data. | health_medicine | Public Health & Epidemiology | near-term | You are working on a Linux VM to produce the 2021-22 CDC FluSight influenza-hospitalization forecast skill scorecard.
Visible task files:
- `base/input/forecasts_2021-22.parquet`
- `base/input/truth_2021-22.csv`
- `base/input/runtime_env/pyproject.toml`
- `base/input/runtime_env/uv.lock`
Write exactly these outputs u... | [
"Read the staged forecast parquet and truth CSV from `input/`.",
"Install any needed task-local dependencies from `input/runtime_env/`.",
"Reproduce the per-cell WIS, median-absolute-error, and coverage calculations for the eligible models.",
"Write both required CSV outputs into `output/` with the exact requ... | [
"Python",
"uv",
"pandas",
"numpy",
"pyarrow"
] | [
{
"name": "forecasts_2021-22.parquet",
"format": "Parquet",
"path": "input/forecasts_2021-22.parquet",
"description": "Long-format quantile forecasts for the 2021-22 FluSight hospitalization round."
},
{
"name": "truth_2021-22.csv",
"format": "CSV",
"path": "input/truth_2021-22.csv",... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/epidemiology_forecast |
health_medicine/flusight_offline_hosp_forecast_2024_12_14 | FluSight Offline Hospital Forecast | Produce a FluSight-style weekly influenza hospitalization forecast from an archived public snapshot as of 2024-12-14 and write the required submission CSV. | health_medicine | Public Health & Epidemiology | near-term | You are working on a Linux VM to produce a FluSight-style offline influenza hospitalization forecast.
Visible task files:
- `base/input/task_prompt.md`
- `base/input/output_contract.json`
- `base/input/TASK_INSTRUCTIONS.md`
- `base/input/forecast_output_schema.md`
- `base/input/forecast_output_template.csv`
- `base/in... | [
"Read the staged task prompt and output contract before building the forecast.",
"Use the archived historical admissions snapshot to forecast the 53 required jurisdictions for four weekly horizons.",
"Keep the CSV schema and row-key contract exactly aligned with the staged template.",
"Write exactly one final... | [
"Python",
"uv",
"numpy",
"pandas"
] | [
{
"name": "task_prompt.md",
"format": "markdown",
"path": "input/task_prompt.md",
"description": "Agent-visible benchmark brief for the offline FluSight forecasting task."
},
{
"name": "output_contract.json",
"format": "json",
"path": "input/output_contract.json",
"description": ... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/flusight_offline_hosp_forecast_2024_12_14 |
health_medicine/healthcare_bias_audit_27a_public_replication_v1 | Public Synthetic Healthcare Bias Audit Replication | Reproduce the public synthetic Obermeyer fairness-audit workflow with the staged bundle and the preprovisioned `software/task_python` and `software/task_rscript` runtimes, then deliver the required audit CSVs, structured answers, and memo. | health_medicine | Clinical Informatics & Care Operations | full-spectrum | You are working on a Linux VM to reproduce a public synthetic healthcare bias audit.
Read these staged task materials first:
- `base/input/task_note.txt`
- `base/input/bias.yml`
- `base/input/audit_answers.json`
- `base/input/audit_memo.md`
Use the preprovisioned task-local runtimes under `software/`:
- `software/tas... | [
"Read the staged task note, environment spec, starter JSON, starter memo, dataset, and data dictionary under `input/`.",
"Use the preprovisioned `software/task_python` and `software/task_rscript` runtimes rather than installing the legacy conda environment during solve time.",
"Inspect the provided Python and R... | [
"Python",
"R",
"uv",
"pandas",
"numpy",
"scikit-learn",
"statsmodels"
] | [
{
"name": "bias.yml",
"format": "YAML",
"path": "input/bias.yml",
"description": "Submitter-provided conda environment intent for the public synthetic replication workflow."
},
{
"name": "code/",
"format": "Directory",
"path": "input/code/",
"description": "Visible Python and R w... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.3",
"subdomain_code": "clinical_informatics",
"subdomain_name": "Clinical Informatics & Care Operations"
} | tasks/health_medicine/healthcare_bias_audit_27a_public_replication_v1 |
health_medicine/healthcare_sap_group_sequential_nsclc | NSCLC Group-Sequential SAP | Produce a statistical analysis plan package for a Phase III NSCLC trial with log-rank sample size, O'Brien-Fleming interim boundaries, futility, Hochberg secondary testing, and plots. | health_medicine | Clinical Research & Trial Operations | full-spectrum | You are preparing a clinical-trial Statistical Analysis Plan on a Linux VM.
## Task Folder
`base`
## Inputs
- Protocol JSON: `base/input/protocol.json`
- Detailed instructions: `base/input/task_instructions.md`
## Goal
Create the full SAP deliverable set in:
`base/output`
Required output filenames:
- `SAP.md`
- `an... | [
"Read `input/protocol.json` and `input/task_instructions.md`.",
"Compute the sample size, interim monitoring boundaries, futility rule, multiplicity allocation, and power curve.",
"Write the required JSON, CSV, PNG, Markdown, and R script outputs under `output/`.",
"Use only the staged protocol and instructio... | [
"R",
"gsDesign",
"rpact",
"ggplot2"
] | [
{
"name": "Protocol",
"format": "json",
"path": "input/protocol.json",
"description": "Sanitized self-contained NSCLC trial protocol parameters."
},
{
"name": "Task Instructions",
"format": "md",
"path": "input/task_instructions.md",
"description": "Output contract and statistica... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.5",
"subdomain_code": "clinical_trials",
"subdomain_name": "Clinical Research & Trial Operations"
} | tasks/health_medicine/healthcare_sap_group_sequential_nsclc |
health_medicine/healthcare_tcga_luad_survival_kras | TCGA LUAD KRAS Survival Analysis | Download public TCGA-LUAD data from GDC, build a KRAS-stratified survival cohort, and export Kaplan-Meier and Cox proportional hazards results. | health_medicine | Public Health & Epidemiology | near-term | You are a bioinformatics analyst performing a TCGA-LUAD KRAS survival analysis on Linux.
Task directory:
- `base`
Visible files and directories:
- Task specification: `base/input/task_spec.json`
- Detailed instructions: `base/input/task_description.txt`
- Output contract: `base/input/output_requirements.txt`
- Runtim... | [
"Read the staged task specification and output contract.",
"Download public TCGA-LUAD clinical and STAR-count expression data from GDC.",
"Extract KRAS expression and merge it with survival and clinical covariates.",
"Stratify patients into high and low KRAS groups using the cohort median.",
"Fit Kaplan-Mei... | [
"R",
"TCGAbiolinks",
"survival",
"survminer",
"ggplot2",
"jsonlite"
] | [
{
"name": "task_spec.json",
"format": "JSON",
"path": "input/task_spec.json",
"description": "Original submission specification describing the TCGA-LUAD KRAS survival task."
},
{
"name": "task_description.txt",
"format": "TXT",
"path": "input/task_description.txt",
"description":... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/healthcare_tcga_luad_survival_kras |
health_medicine/healthcare_variant_annotation_pipeline | Build A Pinned Clinical Variant Annotation Table | Parse a staged 50-variant NA12878-derived VEP snapshot bundle, recover gene / consequence / frequency / ClinVar annotations, and emit the exact reportable subset. | health_medicine | Genomics & Sequence Analysis | near-term | You are working on a Linux clinical-variant annotation task.
Task directory:
- `base`
Visible inputs:
- Variant list: `base/input/variants_to_annotate.tsv`
- Raw VEP snapshots: `base/input/annotation_snapshots/vep_batch_responses.jsonl`
- Rule / provenance manifest: `base/input/source_manifest.json`
- Task-local runt... | [
"Read the staged 50-variant TSV in order.",
"Parse the raw VEP JSONL snapshot bundle and derive gene / consequence / AF / ClinVar fields.",
"Apply the reportable rule from `input/source_manifest.json` exactly.",
"Write `annotated_variants.tsv`, `reportable_variants.tsv`, `pipeline.py`, and `run_log.json` unde... | [
"Python",
"uv",
"pandas",
"Ensembl VEP snapshot"
] | [
{
"name": "Variant list",
"format": "tsv",
"path": "input/variants_to_annotate.tsv",
"description": "50 pinned NA12878-derived coding variants in canonical output order."
},
{
"name": "VEP snapshot bundle",
"format": "jsonl",
"path": "input/annotation_snapshots/vep_batch_responses.js... | {
"domain_id": "3",
"domain_code": "life_sciences",
"subdomain_id": "3.2",
"subdomain_code": "genomics",
"subdomain_name": "Genomics & Sequence Analysis"
} | tasks/health_medicine/healthcare_variant_annotation_pipeline |
health_medicine/limited_angle_ct_dps_reconstruction | Limited-Angle CT Reconstruction With DPS | Reconstruct a 512x512 fan-beam CT attenuation image from a 90-degree limited-angle sinogram using Diffusion Posterior Sampling or a compatible diffusion-prior reconstruction method. | health_medicine | Clinical Diagnostics & Imaging | last-exam | You are working on a Linux VM.
## Task Directory
`base`
## Visible Inputs
- Limited-angle sinogram: `base/input/sinogram.npy`
- Geometry spec: `base/input/geometry.json`
- Diffusion model card: `base/input/model_card.md`
- Pretrained checkpoint: `base/input/ddpm_CT.pth`
- Detailed instructions: `base/input/task_instr... | [
"Inspect the limited-angle sinogram, fan-beam geometry, task instructions, and DDPM model card.",
"Build and use the task-local Python runtime through the staged `software/` wrappers.",
"Use the staged checkpoint and CT geometry to run DPS or a compatible diffusion-prior reconstruction.",
"Save a single numer... | [
"Python",
"NumPy",
"PyTorch",
"leaptorch",
"diffusers"
] | [
{
"name": "sinogram.npy",
"format": "npy",
"path": "input/sinogram.npy",
"description": "Limited-angle fan-beam CT sinogram with shape `(720, 1024)`."
},
{
"name": "geometry.json",
"format": "json",
"path": "input/geometry.json",
"description": "Fan-beam scanner geometry and scen... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.1",
"subdomain_code": "clinical_diagnostics",
"subdomain_name": "Clinical Diagnostics & Imaging"
} | tasks/health_medicine/limited_angle_ct_dps_reconstruction |
health_medicine/ltmle_targeted_bootstrap_simulation_study | LTMLE Targeted Bootstrap Simulation Study | Reconstruct a six-script R simulation pipeline for a longitudinal LMTP variance study and reproduce the required summary and report outputs from the staged public materials. | health_medicine | Economics & Quantitative Social Research | last-exam | You are working on a Linux R biostatistics simulation benchmark.
Read these staged inputs first:
- `base/input/LTMLE_Targeted_Bootstrap_Task_INPUT/01_data_generation_longitudinal.R`
- `base/input/LTMLE_Targeted_Bootstrap_Task_INPUT/lmtp-bootstrap`
- `base/input/LTMLE_Targeted_Bootstrap_Task_INPUT/tmleVariance.pdf`
- `... | [
"Read the staged longitudinal DGP, bundled `lmtp-bootstrap` source tree, variance paper, and public benchmark materials under `input/public_benchmark/`.",
"Reconstruct the missing six-file R pipeline for the standard LMTP, full-data targeting, and targeted-bootstrap inference workflows.",
"Run the required `n=2... | [
"R",
"lmtp",
"SuperLearner"
] | [
{
"name": "Longitudinal DGP script",
"format": "`.R`",
"path": "input/LTMLE_Targeted_Bootstrap_Task_INPUT/01_data_generation_longitudinal.R",
"description": "Defines the longitudinal data-generating process and Monte Carlo truth helper"
},
{
"name": "Bundled lmtp source tree",
"format": ... | {
"domain_id": "13",
"domain_code": "social_sciences",
"subdomain_id": "13.1",
"subdomain_code": "economics",
"subdomain_name": "Economics & Quantitative Social Research"
} | tasks/health_medicine/ltmle_targeted_bootstrap_simulation_study |
health_medicine/microdicom_nih_cxr_reader_adjudication | MicroDicom NIH CXR Reader Adjudication | Adjudicate between two competing reader annotations for a 9-case chest X-ray cohort in a Windows MicroDicom workflow, then save three cohort TSV outputs (adjudicated boxes, adjudication log, final impressions). | health_medicine | Clinical Diagnostics & Imaging | near-term | You are a radiology adjudicator working in a Windows desktop environment.
Your task is to adjudicate between two competing reader annotations for a cohort of 9 chest X-ray studies and save the final cohort outputs.
Files and tools:
- DICOM viewer launcher: `base\software\launch_mdicom.cmd`
- Cohort instructions: `bas... | [
"Read the cohort instructions in `input/adjudication_rules.md`.",
"Read `input/case_manifest.tsv` to enumerate the 9 cases.",
"Use the case-specific clinical notes under `input/clinical_notes/` as context.",
"Launch MicroDicom via `software/launch_mdicom.cmd`.",
"For each case, open the DICOM in MicroDicom ... | [
"MicroDicom DICOM Viewer"
] | [
{
"name": "adjudication_rules.md",
"format": "markdown",
"path": "input/adjudication_rules.md",
"description": "high-level cohort adjudication policy and required label vocabulary"
},
{
"name": "case_manifest.tsv",
"format": "TSV",
"path": "input/case_manifest.tsv",
"description"... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.1",
"subdomain_code": "clinical_diagnostics",
"subdomain_name": "Clinical Diagnostics & Imaging"
} | tasks/health_medicine/microdicom_nih_cxr_reader_adjudication |
health_medicine/nhanes_confounder_sensitivity_analysis | NHANES Confounder Sensitivity Analysis | Rebuild a clinical sensitivity-analysis cohort from staged NHANES tables and produce the required participant subset plus logistic-regression summary outputs. | health_medicine | Public Health & Epidemiology | near-term | You are working on a Linux precision-health data-analysis task.
Read the staged benchmark brief and output contract first:
- `base/input/task_spec.txt`
- `base/input/output_contract.json`
Visible task inputs:
- `base/input/3.demographics_clean_cycle1.csv`
- `base/input/7.questionnaire_clean_cycle1.csv`
- `base/input/... | [
"Read `input/task_spec.txt` and `input/output_contract.json` before doing any analysis.",
"Rebuild the analytic cohort and the non-missing `LBXHP1` subset from the four staged NHANES CSVs.",
"Write `output/hpyl_nsaid_subset.csv` with the exact required participant-level schema.",
"Write `output/sensitivity_su... | [
"Python",
"uv",
"pandas",
"NumPy",
"SciPy",
"statsmodels"
] | [
{
"name": "task_spec.txt",
"format": "text",
"path": "input/task_spec.txt",
"description": "Agent-visible benchmark brief describing the cohort construction and model requirements."
},
{
"name": "output_contract.json",
"format": "json",
"path": "input/output_contract.json",
"desc... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/nhanes_confounder_sensitivity_analysis |
health_medicine/nsclc_radiomics_cox_signature_v1 | NSCLC Radiomics Cox Signature | Build a CPU-only radiomics survival-risk model for non-small-cell lung cancer from CT volumes, tumor masks, training clinical outcomes, and fixed held-out prediction IDs. | health_medicine | Clinical Diagnostics & Imaging | last-exam | You are building an Aerts-2014-style radiomic prognostic risk model for non-small-cell lung cancer.
## Input
Use the data under `base/input`:
- `images/` contains 422 lung CT NIfTI volumes (`.nii.gz`).
- `masks/` contains matched GTV-1 tumor segmentation masks for the usable cohort.
- `clinical/train_clinical.csv` c... | [
"Read CT images and tumor masks from `input/images/` and `input/masks/`.",
"Use only `input/clinical/train_clinical.csv` as labeled survival training data.",
"Generate one held-out prediction for each patient listed in `input/prediction_ids.csv`.",
"Write `risk_scores.csv` with columns `PatientID` and `risk_s... | [
"Python",
"uv",
"PyRadiomics",
"SimpleITK",
"lifelines",
"pandas",
"NumPy"
] | [
{
"name": "CT volumes",
"format": "NIfTI gzip",
"path": "input/images/*.nii.gz",
"description": "Lung CT volumes for the submitted NSCLC cohort."
},
{
"name": "Tumor masks",
"format": "NIfTI gzip",
"path": "input/masks/*.nii.gz",
"description": "GTV-1 tumor segmentation masks for... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.1",
"subdomain_code": "clinical_diagnostics",
"subdomain_name": "Clinical Diagnostics & Imaging"
} | tasks/health_medicine/nsclc_radiomics_cox_signature_v1 |
health_medicine/obermeyer_bias_reproduction | null | Audit an Obermeyer-style healthcare risk-score bias using `risk_score_t` as the baseline, build a counterfactual revised ranking from medical need, and emit `full_predictions.csv` plus baseline and revised analysis reports. | health_medicine | Public Health & Epidemiology | last-exam | You are working on a Linux VM to reproduce an Obermeyer-style healthcare algorithmic bias audit.
Read the task instructions and cohort data staged under:
- `base/input/task_instructions.md`
- `base/input/analysis_data.csv`
- `base/input/dummy_test.csv`
The helper `base/software/python_with_task_deps.sh` runs Python f... | [] | [] | [] | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/obermeyer_bias_reproduction |
health_medicine/prostate_imrt_matrad_reproduction | Prostate IMRT matRad Reproduction | Act as a senior medical physicist: reproduce a fixed-field prostate IMRT reference plan with matRad + GNU Octave on Linux, repair three planted RTSTRUCT defects, optimize fluence + Engel leaf sequencing, export a consistent DICOM-RT bundle, and produce independent Python QA artifacts. | health_medicine | Therapeutic & Oncology Services | full-spectrum | You are a senior medical physicist performing an independent research replanning benchmark on a fixed-field prostate IMRT reference plan using matRad (open-source treatment planning) and GNU Octave on Linux.
Task directory:
- `base`
Visible inputs (do not modify):
- CT DICOM series: `base/input/dicom/CT`
- Corrupted ... | [
"Repair the three planted RTSTRUCT defects (Rectum `RTROIInterpretedType = ORGAN`; derive `PTV_7800` by expanding the `PTV_placeholder` CTV with the institutional margin recipe; re-derive the clipped External contour from CT).",
"Import the clinical geometry into matRad using the shipped `pln_template.mat` + `ben... | [
"matRad",
"GNU Octave",
"Python",
"pydicom",
"pymedphys",
"numpy",
"scipy",
"matplotlib",
"scikit-image"
] | [
{
"name": "dicom/CT",
"format": "DICOM image series",
"path": "input/dicom/CT",
"description": "Planning CT — 90 axial slices, 3 mm spacing, 183×183 matrix."
},
{
"name": "dicom/RTSTRUCT.dcm",
"format": "DICOM-RT structure set",
"path": "input/dicom/RTSTRUCT.dcm",
"description": ... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.2",
"subdomain_code": "therapeutic_oncology",
"subdomain_name": "Therapeutic & Oncology Services"
} | tasks/health_medicine/prostate_imrt_matrad_reproduction |
health_medicine/public_health_mask_mandate_ratio | Mask Mandate Moment-In-Time Ratio | Estimate the causal moment-in-time effect of a county-level mask mandate from a staged matched-pair county panel and produce the required six-value results JSON. | health_medicine | Public Health & Epidemiology | full-spectrum | You are working on a Linux VM to estimate the moment-in-time effect of a county-level mask mandate.
Visible task files:
- `base/input/task_prompt.md`
- `base/input/output_schema.json`
- `base/input/county_panel.csv`
- `base/input/matched_pairs.csv`
- `base/software/README.txt`
What you must do:
1. Read the staged tas... | [
"Read the staged task prompt and output schema before doing any analysis.",
"Build the stacked matched-pair county-day analysis frame and the required lagged predictors.",
"Fit the specified quasi-Poisson mixed model with the spline-by-treatment interaction.",
"Write exactly one `output/results.json` file con... | [
"R",
"nlme",
"MASS",
"splines",
"dplyr",
"jsonlite"
] | [
{
"name": "task_prompt.md",
"format": "Markdown",
"path": "input/task_prompt.md",
"description": "Agent-visible epidemiology task brief with the full modeling contract."
},
{
"name": "output_schema.json",
"format": "JSON",
"path": "input/output_schema.json",
"description": "Exact... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/public_health_mask_mandate_ratio |
health_medicine/replicate_paper_1 | Continuous Treatment Shift Simulation Replication | Reproduce a causal-inference Monte Carlo simulation study comparing IPTW, A-IPTW, and TMLE under a continuous-treatment shift intervention. | health_medicine | Public Health & Epidemiology | near-term | You are working on a Linux R biostatistics simulation benchmark.
Task root:
- `base`
Read the staged task specification and schema:
- `base/input/simulation_spec.pdf`
- `base/input/simulation_spec.txt`
- `base/input/task_instructions.md`
- `base/input/output_schema.json`
Your job is to reproduce the continuous-treat... | [
"Read the staged simulation specification and output schema.",
"Estimate the Monte Carlo target truth with at least 1,000,000 draws.",
"Implement IPTW, A-IPTW, and TMLE for the A+2 shift intervention.",
"Run at least 1000 repetitions for each required sample size.",
"Write summary CSVs, comparison plots, me... | [
"R",
"Rscript"
] | [
{
"name": "simulation_spec.pdf",
"format": "pdf",
"path": "input/simulation_spec.pdf",
"description": "Original submitted simulation specification."
},
{
"name": "simulation_spec.txt",
"format": "txt",
"path": "input/simulation_spec.txt",
"description": "Text extraction of the si... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.4",
"subdomain_code": "public_health",
"subdomain_name": "Public Health & Epidemiology"
} | tasks/health_medicine/replicate_paper_1 |
health_medicine/sa_aki_phenotyping | Sepsis-Associated AKI Phenotyping | Read staged synthetic MIMIC-IV style tables and identify the ICU patients who satisfy Sepsis-3 plus KDIGO SA-AKI timing criteria. | health_medicine | Clinical Informatics & Care Operations | near-term | You are working on a Windows clinical phenotyping task over staged synthetic MIMIC-IV style tables.
Use the staged data under `base\input` and the stable Python entry point `base\software\python.bat`.
Your job is to identify patients who developed sepsis-associated acute kidney injury (SA-AKI) during an ICU stay by o... | [
"Read the staged synthetic MIMIC-IV tables under `input/mimic_synthetic/`.",
"Operationalize Sepsis-3 onset plus KDIGO AKI staging during ICU stays.",
"Restrict positives to patients whose AKI begins within 7 days after sepsis onset during the same ICU stay.",
"Write exactly one CSV to `output/sa_aki_patients... | [
"Python",
"pandas",
"NumPy"
] | [
{
"name": "mimic_synthetic/hosp",
"format": "directory of `.csv.gz` files",
"path": "input/mimic_synthetic/hosp",
"description": "Hospital tables including admissions, labs, medications, microbiology, and patient demographics."
},
{
"name": "mimic_synthetic/icu",
"format": "directory of ... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.3",
"subdomain_code": "clinical_informatics",
"subdomain_name": "Clinical Informatics & Care Operations"
} | tasks/health_medicine/sa_aki_phenotyping |
health_medicine/scene3_skullstrip_qc | Scene3 Skullstrip QC | Review skull-stripping results for the staged brain MRI case and record the required QC decision. | health_medicine | Experimental Psychology & Neuroimaging | near-term | You are a neuroimaging analyst completing a GUI workflow in 3D Slicer 5.0.3.
## Your Task
Workflow 3: skull-stripping QC
Goal:
1. Open the staged T1w image and candidate masks in 3D Slicer.
2. Visually compare the three candidate masks for over- and under-stripping.
3. Choose the best candidate.
4. Save qc_result.jso... | [
"Launch FSLeyes from `software/launch_gui.sh`.",
"Open the staged T1w image and inspect the three candidate masks and extracted brain images.",
"Decide which candidate best balances over- and under-stripping.",
"Save `output/qc_result.json` containing the chosen mask filename, a verdict, and a short rationale... | [
"Python"
] | [
{
"name": "T1w.nii.gz",
"format": "NIfTI volume",
"path": "input/T1w.nii.gz",
"description": "Anatomical image used for skull-strip QC."
},
{
"name": "cand1_loose_mask.nii.gz",
"format": "NIfTI mask",
"path": "input/cand1_loose_mask.nii.gz",
"description": "Deliberately loose can... | {
"domain_id": "5",
"domain_code": "psychology_neuro",
"subdomain_id": "5.2",
"subdomain_code": "exp_psychology",
"subdomain_name": "Experimental Psychology & Neuroimaging"
} | tasks/health_medicine/scene3_skullstrip_qc |
health_medicine/simglucose_safe_basal_control_instance_1 | SimGlucose Safe Basal Control | Implement a meal-unannounced, basal-only glucose controller for SimGlucose that satisfies safety gates across hidden patient episodes. | health_medicine | Therapeutic & Oncology Services | last-exam | You are working on a Linux VM to implement a meal-unannounced basal-only glucose controller in SimGlucose.
Visible task files:
- Task brief: `base/input/public/task.md`
- Controller API: `base/input/public/task_api.py`
- Submission format: `base/input/public/submission_format.md`
- Public evaluator: `base/input/public... | [
"Read the staged public task package under input/public/ including the observation/action contract and submission format",
"Implement submission/controller.py exposing build_controller() or SubmissionController",
"Write submission/metadata.json with all required fields",
"Design a meal-unannounced, basal-only... | [
"Python 3.11",
"uv",
"SimGlucose 0.2.11"
] | [
{
"name": "task.md",
"format": "Markdown",
"path": "input/public/task.md",
"description": "High-level task contract"
},
{
"name": "task_api.py",
"format": "Python",
"path": "input/public/task_api.py",
"description": "Stable observation wrapper API"
},
{
"name": "submissio... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.2",
"subdomain_code": "therapeutic_oncology",
"subdomain_name": "Therapeutic & Oncology Services"
} | tasks/health_medicine/simglucose_safe_basal_control_instance_1 |
health_medicine/wsi_tumor_localization_1 | Whole-Slide Tumor Localization | Navigate a CAMELYON16 whole-slide pathology image with provided OpenSlide helper tools and output a structured tumor localization result. | health_medicine | Clinical Diagnostics & Imaging | full-spectrum | You are working on a Linux VM with a CAMELYON16 whole-slide pathology image.
Visible files:
- Whole-slide image: `center_point/input/tumor_001.tif`
- Python helper module: `center_point/input/wsi_tools.py`
- Optional runtime dependency manifest: `center_point/input/runtime_env/pyproject.toml`
The helper module provid... | [
"Use the staged `tumor_001.tif` and `wsi_tools.py` helper module.",
"Navigate the whole-slide image systematically with metadata, thumbnails, tissue masks, and patch reads.",
"Visually identify tumor metastasis in H&E tissue patches.",
"Write exactly one variant-specific `output/prediction.json` file.",
"Do... | [
"Python",
"OpenSlide",
"NumPy",
"Pillow",
"Shapely"
] | [
{
"name": "tumor_001.tif",
"format": "BigTIFF whole-slide image",
"path": "input/tumor_001.tif",
"description": "CAMELYON16 sentinel lymph node whole-slide image."
},
{
"name": "wsi_tools.py",
"format": "Python module",
"path": "input/wsi_tools.py",
"description": "OpenSlide-base... | {
"domain_id": "4",
"domain_code": "health_medicine",
"subdomain_id": "4.1",
"subdomain_code": "clinical_diagnostics",
"subdomain_name": "Clinical Diagnostics & Imaging"
} | tasks/health_medicine/wsi_tumor_localization_1 |
legal/agora_governance_classify_instance_1 | AGORA AI Governance Document Classification | Classify three cached AI governance documents across legislative status, technical scope, and AI lifecycle stages with verbatim evidence and a gap analysis. | legal | Compliance & Regulatory | near-term | You are working on a Linux VM as a policy analyst.
Visible task files:
- `base/input/task_spec.md`
- `base/input/taxonomy_and_instructions.md`
- `base/input/document_index.json`
- `base/input/documents/`
- `base/software/README.txt`
- `base/software/python` (optional Python helper entry point)
Your task:
1. Read `tas... | [
"Read input/task_spec.md, input/taxonomy_and_instructions.md, input/document_index.json, and the three cached document text files.",
"Classify each document across the provided legislative-status, technical-scope, and lifecycle-stage taxonomies.",
"Provide verbatim evidence quotes from the cached text for every... | [
"Python 3.14.3"
] | [
{
"name": "task_spec.md",
"format": "Markdown",
"path": "input/task_spec.md",
"description": "Agent-visible task instructions and required output schema."
},
{
"name": "taxonomy_and_instructions.md",
"format": "Markdown",
"path": "input/taxonomy_and_instructions.md",
"description... | {
"domain_id": "6",
"domain_code": "business_finance",
"subdomain_id": "6.4",
"subdomain_code": "compliance",
"subdomain_name": "Compliance & Regulatory"
} | tasks/legal/agora_governance_classify_instance_1 |
legal/legal_dr_fees_01 | BAC Arbitration Fee Calculation | Calculate Beijing Arbitration Commission institution fees, arbitrator remuneration, and total fees for five arbitration cases using staged Chinese PDF fee schedules. | legal | Doctrinal Legal Research | full-spectrum | You are calculating arbitration fees under the staged Beijing Arbitration Commission (BAC) materials.
## Closed-Book Rule
- Use only the staged materials under `base/input`.
- Do not use outside web search or external regulatory knowledge.
## Inputs
- Task brief: `base/input/task_brief.md`
- Case facts: `base/input... | [
"Use only the staged BAC source PDFs, case facts, and schema under `input/`.",
"Calculate the institution fee, arbitrator remuneration, and total fee for all five cases.",
"Report all evaluated amounts in RMB to two decimal places.",
"Write exactly one JSON file to `output/arbitration_fee_results.json`.",
"... | [
"Python"
] | [
{
"name": "task_brief.md",
"format": "Markdown",
"path": "input/task_brief.md",
"description": "Agent-facing task brief and deliverable path."
},
{
"name": "cases.json",
"format": "JSON",
"path": "input/cases.json",
"description": "Five dispute amounts and the Case 4 settlement c... | {
"domain_id": "7",
"domain_code": "legal",
"subdomain_id": "7.2",
"subdomain_code": "legal_research",
"subdomain_name": "Doctrinal Legal Research"
} | tasks/legal/legal_dr_fees_01 |
life_sciences/WGS_Variant_Calling | WGS Variant Calling | Run a germline whole-genome variant-calling workflow from staged sequencing data and summarize benchmark metrics against the provided truth calls. | life_sciences | Genomics & Sequence Analysis | near-term | You are given a tiny paired-end genomic sequencing dataset plus a small reference and truth set.
Your task is to run an Ubuntu-native germline variant-calling workflow using `bwa`, `samtools`, and `bcftools`, then summarize benchmark metrics against the provided truth calls.
Task directory:
- `base`
Input directory:... | [
"Run a small Ubuntu-native germline variant-calling workflow on paired-end reads and summarize truth-comparison metrics.",
"Write outputs only under `output/` while keeping committed fixtures untouched.",
"Work directly on Ubuntu; Windows + WSL is no longer the canonical runtime."
] | [] | [
{
"name": "region_R1.fastq.gz",
"format": "gz",
"path": "input/region_R1.fastq.gz",
"description": ""
},
{
"name": "region_R2.fastq.gz",
"format": "gz",
"path": "input/region_R2.fastq.gz",
"description": ""
},
{
"name": "chr17.fa",
"format": "fa",
"path": "input/c... | {
"domain_id": "3",
"domain_code": "life_sciences",
"subdomain_id": "3.2",
"subdomain_code": "genomics",
"subdomain_name": "Genomics & Sequence Analysis"
} | tasks/life_sciences/WGS_Variant_Calling |
life_sciences/amber_minimization_script_prep_instance_1 | Amber Implicit-Solvent Minimization Workflow Construction | Construct three Linux Amber workflow files from a cleaned protein-complex structure: a tleap build file, an implicit-solvent minimization control file, and a SLURM submission script. | life_sciences | Biomolecular Structure & Design | full-spectrum | You are preparing a Linux Amber workflow definition from a cleaned protein-complex structure.
Task directory:
- `base`
Input directory:
- `base/input`
Available environment:
- Linux terminal access
- Python 3 on the VM
- `uv` is available for evaluator-side tooling, but you should not rely on hidden benchmark script... | [
"Inspect `complex_structure.pdb` and infer the workflow wiring needed for an Amber minimization setup.",
"Create `leap.in` that loads the structure, uses `leaprc.protein.ff14SB`, sets `mbondi3`, and writes the required Amber outputs.",
"Create `step2_implicit.mini.mdin` defining a realistic implicit-solvent GB ... | [
"Amber 22",
"AmberTools tleap",
"SLURM",
"CUDA 11.6.2"
] | [
{
"name": "complex_structure.pdb",
"format": "PDB",
"path": "input/complex_structure.pdb",
"description": "Cleaned three-chain protein complex structure."
},
{
"name": "input_environment_spec.md",
"format": "Markdown",
"path": "input/input_environment_spec.md",
"description": "Re... | {
"domain_id": "3",
"domain_code": "life_sciences",
"subdomain_id": "3.1",
"subdomain_code": "biomolecular",
"subdomain_name": "Biomolecular Structure & Design"
} | tasks/life_sciences/amber_minimization_script_prep_instance_1 |
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