optimize

Turn log analysis misses into actionable rule edits by clustering open entries from threads_skill_learnings.log into sub_skill and category groups. The process drafts concrete edits with exact file locations, before/after text, and supporting evidence quotes for review.

The best way to propose sub-skill rule changes from captured log data is to anchor edits to shared knowledge and rules, maintaining consistency across sub-skills. Proposals include exact file locations, before/after text, and supporting quotes for reviewer approval.

Apply versioning and backups when updating sub-skill rules by requiring explicit user approval before applying any change. The system manages safe backups, performs version bumps, and maintains a changelog-like trace of addressed entries and superseded items for auditability.

Yes, you can review and group log misses by category before modifying workflow files. The optimization process surfaces actionable improvement areas by clustering open misses by sub_skill and category, drafting proposals that await explicit approval before any edits are applied.

Updating sub-skill rules requires explicit user approval to ensure changes are proposed with evidence and applied safely. This requirement guarantees that safe backups and version bumps are executed intentionally, maintaining traceable records of superseded entries.