darwin-skill

To optimize Claude Code Skills automatically, use an autonomous optimization approach that evaluates structure and effectiveness using an 8-dimensional rubric. It applies a hill-climbing algorithm with git version control and user confirmation to refine skills.

The 8-dimensional rubric for skill evaluation is a structured assessment framework that combines static analysis and real-world testing. It measures Claude Code Skill quality to ensure both structure and functionality are optimized during the refinement process.

Yes, optimizing Claude Code Skills requires human intervention for final decision-making. The autonomous hill-climbing algorithm proposes changes, but each optimization step requires explicit user confirmation to ensure the modifications meet desired standards.

Yes, you need git and python installed in your environment to run autonomous skill optimization. Git provides version control for tracking changes, while python executes the optimization scripts and assessment logic.

The best way to evaluate and improve Claude Code Skill structure is applying a hill-climbing optimization algorithm. This method combines static analysis with real-world testing, using git version control to manage structural changes and ensure quality improvements.