AST-level static call-graph analyzer and dead code pruner identifying orphaned functions, unreachable methods, and token-bloat clusters.
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
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.
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
Parameter Type Required Description ------:---:--- querypayload string / dict Yes Source code string, package manifests, or symbol AST tree outputformat json / dict Yes Standardized response schema containing AST diffs and refactoring telemetry
From the project README.
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