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| // MCO Core Configuration - Meta-Educational Orchestration System | |
| // Teaching an agent about MCO Protocol while orchestrating it to CREATE an MCO workflow | |
| // Recursive demonstration: MCO orchestrates agent to understand and replicate MCO | |
| @workflow "MCO Meta-Teacher: Code Review Agent Creator" | |
| > You are currently being orchestrated by the MCO (Model Configuration Orchestration) Protocol RIGHT NOW as you read this. | |
| > Notice how each section of this file builds upon the previous section - this is SNLP's progressive revelation structure. | |
| > Your mission: Create a focused code review agent that demonstrates MCO orchestration without overwhelming complexity. | |
| > This is meta-learning: Learn MCO by experiencing MCO orchestration while building MCO workflows. | |
| @description "Revolutionary meta-educational workflow: Learn orchestration by being orchestrated" | |
| > MCO's breakthrough innovation combines TWO powerful concepts: | |
| > 1. PROGRESSIVE REVELATION STRUCTURE: Each SNLP section builds context from the previous section, creating organic information flow | |
| > 2. ITERATIVE ORCHESTRATION LOOPS: Agent attempts → MCO evaluates → "Try again, but better" → repeat until success criteria met | |
| > Together, these create reliable autonomous agents that maintain context while continuously improving output quality. | |
| > You will experience both mechanisms firsthand, then encode them into a practical code review agent. | |
| @version "1.0.0" | |
| // Data Section - Building Context Progressively | |
| @data: | |
| educational_context: "You are learning MCO by being orchestrated BY MCO to CREATE MCO workflows" | |
| > This creates metacognitive learning - you understand the system by experiencing it while building it. | |
| target_agent_name: "Smart Code Review Agent" | |
| > Building on educational context: The agent you create will be practical, not overly complex. | |
| target_agent_purpose: "Conduct reliable code reviews using MCO orchestration and MCP data sources" | |
| > Building on agent name: Focus on reliability through MCO's iterative improvement rather than complex features. | |
| code_review_scope: "Basic security checks, style validation, logic review, performance suggestions" | |
| > Building on purpose: Keep the agent focused on core review tasks that demonstrate MCO's power. | |
| // SNLP Progressive Revelation Pattern You Must Follow | |
| snlp_structure_explanation: | | |
| CRITICAL: SNLP follows progressive revelation - each section builds context from previous sections: | |
| @workflow introduces the concept | |
| > @description expands on the workflow concept | |
| > @data starts with simple context, then each data point builds on previous data points | |
| > @agents builds on the data context to define execution steps | |
| > Each > block references and expands on information from previous > blocks | |
| This creates organic information flow that prevents context loss and cognitive overload. | |
| // Code Review Agent Specifications (Practical, Not Overwhelming) | |
| target_agent_steps: | | |
| EXACTLY 6 STEPS (manageable complexity): | |
| 1. "Connect to MCP server and gather code repository data" | |
| 2. "Scan code for basic security vulnerabilities and style issues" | |
| 3. "Analyze logic flow and identify potential bugs" | |
| 4. "Generate performance improvement suggestions" | |
| 5. "Compile findings into structured review report" | |
| 6. "Validate review completeness against success criteria" | |
| > Building on agent steps: Each step will be subject to MCO's evaluation loops - attempt → evaluate → improve → repeat. | |
| mcp_integration_requirements: | | |
| SIMPLE MCP CONNECTION PATTERN: | |
| - Connect to source code MCP server (GitHub/GitLab) | |
| - Use JSON-RPC over stdio for data retrieval | |
| - Handle connection errors gracefully with retry logic | |
| - MCO orchestrates iterative use of MCP data throughout review process | |
| > Building on MCP requirements: The agent doesn't need complex integrations - MCO's orchestration makes simple connections powerful. | |
| // Agents Section - Your Orchestrated Learning Journey | |
| @agents: | |
| mco_meta_orchestrator: | |
| name: "MCO Educational Orchestrator" | |
| description: "Teaches MCO through hands-on orchestrated creation experience" | |
| model: "claude-3-5-sonnet-20241022" | |
| > Building on workflow and data context: This agent will guide you through creating a practical code review agent. | |
| steps: | |
| - "Experience MCO's progressive revelation: notice how each section builds context from previous sections" | |
| - "Experience MCO's iterative orchestration: complete task → receive evaluation → improve → repeat" | |
| - "Design simple but effective code review workflow using 6 focused steps" | |
| - "Generate mco.core following progressive revelation pattern: each section builds on previous sections" | |
| - "Create mco.sc with practical success criteria that MCO will use for evaluation loops" | |
| - "Build mco.features with focused capabilities that enhance core review functions" | |
| - "Develop mco.styles with clear presentation formats for review output" | |
| - "Validate complete SNLP workflow demonstrates both progressive revelation AND iterative orchestration" | |
| > Building on agent steps: Each step you complete will be evaluated by MCO using the same iterative process you're encoding. | |
| > Notice the progressive revelation in action: Each > block builds on information from previous blocks and sections. | |
| > When MCO says "Try again, but better," study the feedback mechanism - this is what makes autonomous agents reliable. | |
| > Your lived experience of progressive revelation + iterative orchestration IS the blueprint for what you're creating. | |
| // Error Handling - MCO's Persistent Improvement Philosophy | |
| @error_handling: | |
| context_loss: | |
| condition: "Agent loses track of progressive revelation structure in generated SNLP files" | |
| mco_response: "Try again, but better: Your SNLP sections don't build context progressively - each section should reference and expand on previous sections" | |
| agent_action: "Review how THIS file builds context progressively, apply the same pattern" | |
| > Building on error handling: Context preservation is crucial - progressive revelation prevents information loss. | |
| insufficient_orchestration: | |
| condition: "Generated workflow lacks proper MCO evaluation points and improvement loops" | |
| mco_response: "Try again, but better: Your workflow needs MCO evaluation after each step with 'try again, but better' capability" | |
| agent_action: "Study how MCO is currently orchestrating YOU, encode that same pattern" | |
| > Building on context loss handling: Iterative improvement requires explicit evaluation and feedback mechanisms. | |
| overcomplexity: | |
| condition: "Generated code review agent is too complex for effective demonstration" | |
| mco_response: "Try again, but better: Simplify the agent - focus on demonstrating MCO's power, not feature complexity" | |
| agent_action: "Keep the agent practical and focused - let MCO's orchestration provide the sophistication" | |
| > Building on orchestration handling: MCO makes simple agents powerful through iterative improvement - complexity isn't needed. | |
| > Building on all previous context: This error handling demonstrates MCO's core innovation. | |
| > Progressive revelation structure prevents context loss and cognitive overload. | |
| > Iterative orchestration loops ensure continuous improvement until true quality is achieved. | |
| > Together, these mechanisms create autonomous agents that are both reliable and sophisticated. | |
| > Your job: Create a practical code review agent that demonstrates these principles in action. |