Skill Evolution Manager

To update AI skills from conversation feedback, the workflow analyzes session insights, converts them into an evolution JSON payload, and stitches them into SKILL.md. This preserves learned best practices across versions while maintaining a deterministic history.

Version control for evolving AI workflows works by persisting changes to each Skill via evolution.json and stitching them into SKILL.md. It supports per-skill isolation, incremental merges, and safe cross-version alignment for recoverable updates.

You can propagate documentation updates across related skills through cross-skill alignment. This feature propagates improvements across related Skills to maintain consistency and ensure best practices are aligned throughout the library.

The best way to extract knowledge management insights from session logs is to trigger the evolution workflow after a session. It performs session review, identifies strengths and gaps, and captures feedback into a structured evolution JSON payload.

The skill evolution approach supports incremental merges without losing history. It ensures deterministic history and recoverable updates by isolating per-skill changes and safely aligning cross-version modifications in evolution.json.

You should not use an automated skill evolution manager when your sessions lack sufficient feedback for experience extraction, or when your documentation framework does not support stitching updates into SKILL.md files.