AgentSkillsCN

dev-intelligence-orchestrator Skill

dev-intelligence-orchestrator技能

SKILL.md

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 <log_file> <project_type> [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 <target> <focus> <project_root>
# 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 <target> <focus> <project_root>
# 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 <project_root> <test_path> <coverage>

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 <operation> [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)

code
🔍 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)

code
🔍 Checking for accumulated knowledge...
✓ Found 1 relevant knowledge item(s)
✓ Using learned configuration from: <prompt_id>
🔧 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