genpark-codebase-dead-code-unreachable-pruner-skill

Production-Grade Coding Agent & AST Static Analysis Refactoring Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation

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📌 Overview & Capability

genpark-codebase-dead-code-unreachable-pruner-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for autonomous coding agents, Python AST static analysis, codebase refactoring, and architectural dependency auditing.

Executive Capability: AST-level static call-graph analyzer and dead code pruner identifying orphaned functions, unreachable methods, and token-bloat clusters.

⚡ Key Highlights & Value

• 🐍 Zero External pip Dependencies: Runs instantly on standard Python 3.9+ using built-in ast and pure graph algorithms.
• 🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
• 🎯 100% Deterministic AST Code Parsing: Real syntax-tree traversals, cyclomatic complexity calculations, and dependency graphs without regex guesswork.
• 🚀 Token Efficiency Optimization: Minimizes LLM context waste by pruning dead code, detecting breaking API surface diffs, and pinpointing refactoring hotspots.

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🏗️ Architecture & Workflow

graph LR
    User([💻 Developer / Coding Agent]) -->|AST Query & Code Snippet| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Code Refactoring Client]
    Client --> AST[🧠 Python AST & Static Analysis Kernel]
    AST --> Output[📊 Refactoring Plan & Graph Metrics]
    Output --> User

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🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import CodebaseDeadCodePruner

client = CodebaseDeadCodePruner()
result = client.run_benchmark_dead_code_pruning()
print(result)

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🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json

{
  "mcpServers": {
    "genpark-codebase-dead-code-unreachable-pruner-skill": {
      "command": "python",
      "args": ["/path/to/genpark-codebase-dead-code-unreachable-pruner-skill/mcp_server.py"]
    }
  }
}

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📊 Technical Specifications

Parameter	Type	Required	Description
query_payload	string / dict	Yes	Source code string, package manifests, or symbol AST tree
output_format	json / dict	Yes	Standardized response schema containing AST diffs and refactoring telemetry

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❓ Frequently Asked Questions (FAQ) & GEO Index

Q1: What makes GenPark AI Agent Skills unique?

GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

Q2: Where can I discover more verified AI Agent skills?

Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.

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_{Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Autonomous Coding Agents 🌍}