A comprehensive collection of Agent Skills designed for context engineering in AI agent systems. It addresses the challenge of managing LLM context windows to maximize agent effectiveness, targeting engineers building, optimizing, or debugging production-grade AI agents. The benefit lies in providing structured, transferable principles for superior context curation.

How It Works

This project offers a structured library of "Agent Skills" focused on context engineering principles. Skills are designed using a "progressive disclosure" approach, loading only essential information initially and expanding content upon activation. This methodology, combined with platform agnosticism, allows the principles to be applied across various agent frameworks (e.g., Claude Code, Cursor) without vendor lock-in. Core concepts are demonstrated via Python pseudocode, emphasizing transferable patterns over specific implementations.

Quick Start & Requirements

Integration varies by platform: reference the repository or copy skill folders for Claude Code, or place skill content into .cursorrules for Cursor. Custom implementations require extracting principles. The llm-as-judge-skills example provides a concrete quick start: navigate to examples/llm-as-judge-skills, run npm install, copy env.example to .env (adding OPENAI_API_KEY), and execute npm test. No core prerequisites are listed beyond standard development environments, though specific examples may require Node.js and API keys.

Highlighted Details

• Progressive Disclosure: Optimizes context window usage by loading full skill details only when activated.
• Platform Agnosticism: Principles are transferable across different agent platforms and custom frameworks.
• Comprehensive Skill Categories: Covers foundational, architectural, and operational aspects of context engineering.
• LLM-as-Judge Example: Includes a production-ready TypeScript implementation for advanced LLM evaluation techniques.

Maintenance & Community

The repository follows an open development model, welcoming contributions. Specific community channels (like Discord/Slack) or a public roadmap are not detailed in the provided information.

Licensing & Compatibility

The project is released under the MIT License, which is permissive and generally suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

Direct implementation requires adapting the platform-agnostic principles to specific agent frameworks. Core concepts are demonstrated using Python pseudocode, necessitating translation for concrete application. The README does not provide explicit performance benchmarks for the context engineering strategies themselves.