skill-create

To generate skills from Git history, you analyze commit patterns and filter accumulated instincts by category to auto-produce a formal Markdown skill artifact with metadata for AI agent reuse. This consolidates recurring code-change patterns into a standardized, reusable format.

Codifying recurring code-change patterns involves identifying common signals in commit history and instincts, then translating them into a formal Skill Unit artifact. This enables knowledge standardization and accelerates onboarding by converting manual practices into reusable automation metadata.

Yes, you can automate skill metadata creation by analyzing recent commit history and instinct signals to identify common patterns, which generates a formal Markdown skill artifact including metadata for reuse by other agents and teams.

Converting Git history into a skill unit reduces manual effort by automatically analyzing commits and filtering instincts to identify recurring patterns, directly producing a standardized Markdown artifact that eliminates the need to manually document project practices.

No specific dependencies are required to analyze commits and generate the Markdown skill artifact. The process operates independently to evaluate Git history and instinct signals for auto-generation.

Avoid auto-generating skills from commit history when your project lacks sufficient recurring code-change patterns or accumulated instincts, as the process requires identifiable signals to produce a meaningful and reusable Markdown skill artifact.