skill-workflow-integration

You can automate skill evaluation by running an integrated workflow that scans all skills, scores them across an 8-dimension rubric, and evolves low-scoring ones using AI-driven optimization. It syncs improvements back to GitHub to maintain a production-ready library.

The ratchet mechanism ensures quality during AI-driven skill optimization by preventing regression. It evaluates skills across eight dimensions, applies evolutionary improvements, and generates governance-ready reports to track progress without degrading existing functionality.

Hermes Skills quality is assessed by scanning the repository and evaluating each skill against an 8-dimension Darwin.skill rubric. This generates comprehensive governance-ready reports that highlight low-scoring areas for targeted improvements.

Yes, a skill-manager is required for categorization and Git operations. You also need a frontmatter-compliant SKILL.md file for each skill, plus modular evaluator and evolver components to enable the end-to-end automation workflow.

Yes, the workflow syncs evolved improvements back to GitHub automatically. After scanning, evaluating, and evolving skills under the ratchet mechanism, it maintains a production-ready skill library by committing updates directly to the repository.

The workflow requires strict prerequisites: a frontmatter-compliant SKILL.md, a skill-manager for Git operations, and modular evaluator and evolver components. Without these structural elements, the end-to-end automation cannot function properly.