Optimize AI agent skills using a 9-dimension rubric and hill-climbing loop.
npx skills add https://github.com/zhaojun1/skills --skill darwin-skill-zhaojun1
Please help me install this Agent Skill. Skill: darwin-skill Source: https://github.com/zhaojun1/skills/tree/main/darwin-skill Command: npx skills add https://github.com/zhaojun1/skills --skill darwin-skill-zhaojun1
This Skill solves the problem of inconsistent and low-quality AI agent skills by providing an autonomous, evidence-based optimization loop that ensures skills are effective, reliable, and safe.
Use the darwin-skill to optimize all skills in the current repository.
High-intent search queries and answers about installing and using this skill.
To optimize AI agent skills, you can use an autonomous hill-climbing loop that applies a 9-dimension rubric to iteratively improve SKILL.md files, ensuring structural clarity and operational effectiveness across runtime environments.
A 9-dimension rubric for agent skill quality assurance is an evidence-based scoring system that evaluates skills on structure, effectiveness, and meta-skill risk management to ensure they are reliable and safe.
Autonomous hill-climbing improves AI agent workflows by iteratively modifying SKILL.md files using git version control and independent judge agents to perform blind evaluations of skill performance.
Yes, you need git version control to audit and score existing agent skills, as it is required to manage the iterative improvements and track changes during the autonomous optimization loop.
Yes, you can use independent judge agents to perform blind evaluations of agent performance, verifying critical improvements before final implementation to ensure high-quality AI agent skills.
A limitation of autonomous skill optimization is that it requires a human-in-the-loop to verify critical improvements before final implementation, ensuring changes are safe and effective before deployment.
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