/dev-intelligence-orchestrator

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:**

./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:**

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:**

./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:**

./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:**

./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:**

./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:**

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: <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

# 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:

{
  "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

# 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

# Security-focused analysis
./analyze_python.sh src/auth.py security .

# General analysis
./analyze_python.sh src/utils.py general .

Parse Build Errors

# Analyze build log
python3 parse_build_errors.py build.log esp32 platformio

# Will learn from similar error patt