toolkit-evolution

Evidence-based improvement requires concrete signals like routing failures, git churn, and learning database records to block speculative changes. This ensures that proposed updates to skills and routing are validated through deterministic checks and A/B testing before promotion.

You run a self-improvement cycle by executing a discovery process to find missing skills, diagnosing root causes from repo history, building implementations on isolated feature branches, and validating outcomes with A/B testing before promoting via PR.

Yes, you can validate new features by implementing top winners on isolated feature branches, running deterministic validation checks, and comparing outcomes against a baseline. This A/B testing ensures only proven updates get promoted to the main toolkit.

Yes, the process requires concrete evidence such as routing failures, git churn signals, and learning database insights for every proposal. This evidence-gated approach blocks speculative improvements and ensures updates are driven by measured workflow gaps.

Automated toolkit evolution is limited by its reliance on concrete evidence; it blocks speculative improvements. It also requires multi-persona critique or a defined fallback, isolated feature-branch builds, and full cycle learnings recorded to prevent unmeasured workflow gaps.