Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
By leveraging LangChain, the application isolates runtime dependencies while maintaining low-latency execution traces across multi-step LLM operations.
Clone headroomlabs-ai/headroom from GitHub
Install dependencies with pip install -r requirements.txt (or uv sync)
Copy .env.example to .env and add your API keys
Run the skill server and connect your AI client via stdio or HTTP
Anthropic's official Claude Skills for working with document formats — fill, merge, and extract data from PDFs, Word files, PowerPoint decks, and Excel spreadsheets inside agent workflows.
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
