skill-evolution-manager

Automating AI skill improvement involves capturing user feedback and dialogue analysis to update skill documentation automatically. This Skill merges those insights into evolution.json and stitches learned best practices directly into SKILL.md files.

Automated documentation update works by running Python scripts that merge user feedback into evolution.json, then stitching those learned constraints and workarounds directly into SKILL.md. This ensures skill documentation reflects real-time performance.

Yes, you can use Python scripts to integrate user preferences into skill management. The scripts process dialogue analysis and merge captured fixes or custom prompts into evolution.json, persistently storing learned best practices.

The best way to record workarounds is to capture the correct usage or fixes from the conversation dialogue. This Skill systematically integrates those user preferences into the skill's logic, ensuring smoother future interactions.

No dependencies are required to run the skill evolution manager. It operates independently using internal Python scripts to process feedback, update evolution.json, and enhance your existing SKILL.md documentation.