What problem does it solve?
Analyzes patches, project context, and codebase patterns to proactively improve AI Factory skills, reducing recurring mistakes and tightening guards across the skill set.
Core Features & Use Cases
- Patch-driven evolution: reads .ai-factory/patches to extract problems, root causes, prevention rules, and tags.
- Gap analysis: compares patch findings with existing SKILL.md guidance to identify missing guards, plan updates, and implementation checks.
- Context-driven evolution: uses project DESCRIPTION and conventions to tailor skill improvements to the current tech stack and coding standards.
- Use Case: When a set of patches reveals a recurring null-check issue in data handling, evolve /aif-evolve to add a null-guard recommendation to the /aif-fix skill.
Quick Start
Run the evolution workflow to start analyzing patches and project context, then apply approved improvements to installed skills.