⚡ ContextSkeleton

Zero-latency Structural Code Folding CLI, Model Context Protocol (MCP) Server & Token Optimizer for AI Agents (Cursor, Claude Code, Windsurf, AGY).

ContextSkeleton folds function and class implementations while preserving top-level signatures, interface definitions, exported types, and docstrings across JavaScript, TypeScript, Python, Go, and C-style languages.

Token reduction scales with function size and implementation depth — typically 0% on trivial 1-2 line utility functions (comment-marker overhead exceeds the savings), 45-70% on production-sized modules with substantial function bodies, and up to 90%+ on large, implementation-heavy files. Across our internal benchmark set spanning small, medium, and large files in TypeScript, Python, Go, and Rust, the blended average was 67%.

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🚀 Quick Start

1. Run CLI Scan

Scan your project to view code compression and token savings metrics:

npx context-skeleton scan

2. Copy Formatted Context for AI Chat Prompts

Dump the compressed skeleton context directly to stdout for Cursor, Claude Code, or Windsurf:

npx context-skeleton copy > context.md

3. Add to PRs & .cursorrules / CLAUDE.md

Inject automated token-savings badges into your repository:

npx context-skeleton badge

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🤖 Model Context Protocol (MCP) Integration

Integrate ContextSkeleton natively with Claude Code, Cursor, or AGY agents.

Add to your claude_desktop_config.json or .cursorrules:

{
  "mcpServers": {
    "context-skeleton": {
      "command": "npx",
      "args": ["-y", "context-skeleton-mcp"]
    }
  }
}

Exposed MCP Tools:

• get_repo_skeleton: Returns folded structural skeleton of target repository.
• unfold_symbol: Retrieves exact implementation of a specific function or class on demand.
• get_token_savings: Returns exact token & prompt cost savings metrics.

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📊 Features & Benchmarks

Feature	Raw Codebase	With ContextSkeleton	Benefit
Blended Benchmark (9 files)	6,183 tokens	2,028 tokens	67.2% Blended Savings
Production Modules (88-135 lines)	~550 tokens / file	~220 tokens / file	45% to 70% Reduction
Large Files (500+ lines)	2,963 tokens	236 tokens	92.0% Reduction
Syntax Errors	Common (unpruned)	0% (Valid Signatures)	Syntactically intact

Note: Token reduction scales with function size and implementation depth — typically 0% on trivial 1-2 line utility functions (comment-marker overhead exceeds the savings), 45-70% on production-sized modules with substantial function bodies, and up to 90%+ on large, implementation-heavy files. Across our internal benchmark set spanning small, medium, and large files in TypeScript, Python, Go, and Rust, the blended average was 67%.

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💻 Tech Stack ($0 Infra Cost)

• Engine: Zero-dependency Node.js Structural Signature Pruner & Token Counter
• MCP Server: Stdio JSON-RPC 2.0
• Web App: Single-Source HTML5 + Modern CSS + Pure JS

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📄 License

MIT License © 2026 ContextSkeleton