# dev-intelligence-orchestrator Skill ## Overview The `dev-intelligence-orchestrator` skill provides intelligent development tool orchestration with **self-improving learning capabilities** through mcp-prompts integration. ## Features ### Core Capabilities - **Project Type Detection**: Automatically identifies languages, frameworks, and project nature - **Intelligent Build Error Analysis**: Parses and diagnoses compilation errors with pattern recognition - **Static Code Analysis**: C++ (cppcheck) and Python (pylint) analysis with learned configurations - **Test Execution**: Framework-aware test running with pytest, PlatformIO, and more - **Self-Improving Learning**: Captures successful configurations and reuses them automatically ### Learning Loop Every tool execution follows this pattern: 1. **BEFORE**: Query mcp-prompts for learned configurations 2. **DURING**: Use learned config if available, fallback to defaults 3. **AFTER**: Capture successful configurations for future use 4. **NEXT**: Automatically use learned configurations ## Scripts ### Core Scripts #### `detect_project_type.sh` Detects project characteristics: - Languages (C++, Python, Kotlin, Java) - Frameworks (PlatformIO, CMake, Conan, Gradle) - Test frameworks (pytest, gtest, JUnit) - Project nature (embedded, Android, desktop) - FlatBuffers usage **Usage:** ```bash ./detect_project_type.sh [directory] ``` #### `parse_build_errors.py` Intelligent build error analysis with learning: - Parses compilation, linking, dependency, and schema errors - Generates diagnosis and recommendations - Learns from similar error patterns - Captures novel error patterns for future reference **Usage:** ```bash python3 parse_build_errors.py [build_system] ``` #### `analyze_cpp.sh` C++ static analysis with cppcheck + learning: - Queries for learned cppcheck configurations - Uses learned flags when available - Captures successful configurations - Updates confidence based on success rate **Usage:** ```bash ./analyze_cpp.sh # focus: security|performance|memory|general ``` #### `analyze_python.sh` Python static analysis with pylint + learning: - Queries for learned pylint configurations - Applies learned options - Captures successful configurations - Tracks success metrics **Usage:** ```bash ./analyze_python.sh # focus: security|performance|style|general ``` #### `run_tests.sh` Test execution with framework detection + learning: - Auto-detects test framework (pytest, PlatformIO, gtest, Gradle) - Queries for learned test configurations - Captures successful test patterns - Supports coverage reporting **Usage:** ```bash ./run_tests.sh ``` ### Supporting Scripts #### `mcp_query.sh` HTTP API wrapper for mcp-prompts: - Health checks - List/search prompts - Get specific prompts - Create/update prompts - Graceful degradation when server unavailable **Usage:** ```bash ./mcp_query.sh [args...] # operations: health|list|get|search|create|update|apply ``` #### `seed-tool-config-prompts.js` Creates initial seed prompts for tool configurations: - cppcheck configurations (embedded, desktop) - pylint configurations (general, security) - pytest configurations - Ready for learning system validation **Usage:** ```bash node seed-tool-config-prompts.js ``` ## Learning Behavior ### First Execution (No Knowledge) ``` 🔍 Checking for accumulated knowledge... ℹ No accumulated knowledge yet, using defaults (will capture learnings) 🔧 Running tool... 💡 Capturing successful configuration... ✓ Configuration captured for future use ``` ### Subsequent Executions (With Knowledge) ``` 🔍 Checking for accumulated knowledge... ✓ Found 1 relevant knowledge item(s) ✓ Using learned configuration from: 🔧 Running tool... ✓ Validating learned configuration... ✓ Configuration validated (success_count: 2, confidence: medium) ``` ### Confidence Levels - **low**: 1 successful use - **medium**: 2-3 successful uses - **high**: 4+ successful uses ## Configuration ### Prerequisites - mcp-prompts server running (optional, graceful degradation if unavailable) - Analysis tools installed: pylint, cppcheck, pytest (as needed) - jq for JSON parsing ### Environment Variables - `MCP_PROMPTS_URL`: mcp-prompts server URL (default: http://localhost:3000) - `PROJECT_ROOT`: Project root directory (default: current directory) ### Server Setup ```bash # Start mcp-prompts server with file storage MODE=http STORAGE_TYPE=file PROMPTS_DIR=./data pnpm start:http ``` ## Integration with mcp-prompts ### Prompt Structure Tool configurations are stored as prompts with this structure: ```json { "name": "cppcheck-config-embedded-esp32-memory-20251231", "description": "Successful cppcheck configuration for embedded-esp32 memory analysis", "template": { "project_type": "embedded-esp32", "focus": "memory", "cppcheck_flags": ["--enable=warning,performance", "--std=c++11"], "success_count": 3, "confidence": "medium", "last_used": "2025-12-31T18:00:00Z" }, "category": "tool-config", "tags": ["cpp", "cppcheck", "memory", "embedded-esp32", "validated"] } ``` ### Learning Domains - **Tool Configurations**: cppcheck, pylint, pytest settings - **Error Patterns**: Build error diagnosis and fixes - **Project Patterns**: Project-specific optimizations - **Workflow Patterns**: Successful development workflows ## Usage Examples ### Analyze C++ Code ```bash # First run - captures learning ./analyze_cpp.sh src/main.cpp memory . # Second run - uses learned config ./analyze_cpp.sh src/main.cpp memory . ``` ### Analyze Python Code ```bash # Security-focused analysis ./analyze_python.sh src/auth.py security . # General analysis ./analyze_python.sh src/utils.py general . ``` ### Parse Build Errors ```bash # Analyze build log python3 parse_build_errors.py build.log esp32 platformio # Will learn from similar error patterns ``` ### Run Tests ```bash # Run with coverage ./run_tests.sh . tests/ true # Run specific test ./run_tests.sh . tests/test_auth.py false ``` ## Graceful Degradation All scripts handle mcp-prompts unavailability gracefully: - If server not running: Uses defaults, warns user - If query fails: Uses defaults, continues execution - Learning is **optional**, not required for tool execution ## Success Criteria The skill is successful when: 1. ✅ Claude reports learning status on every execution 2. ✅ Second analysis is faster/better than first due to learned configuration 3. ✅ User sees knowledge accumulating through visible capture messages 4. ✅ Confidence increases as more patterns are validated 5. ✅ Cross-project knowledge sharing works ## Files Included - `detect_project_type.sh` - Project detection - `parse_build_errors.py` - Build error analysis with learning - `analyze_cpp.sh` - C++ analysis with learning - `analyze_python.sh` - Python analysis with learning - `run_tests.sh` - Test execution with learning - `mcp_query.sh` - mcp-prompts API wrapper - `seed-tool-config-prompts.js` - Seed prompt generator - `SKILL.md` - This documentation ## Version **Version**: 2.0.0 (with Learning Loop) **Last Updated**: 2025-12-31 **Status**: Production Ready

dev-intelligence-orchestrator Skill

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