What problem does it solve?
This Skill enables teams to automatically implement high-confidence improvements generated by /meta-learn while ensuring safety through dry-run checks, backups, and rollback mechanisms. It eliminates manual, error-prone adoption of optimizations and accelerates secure deployment.
Core Features & Use Cases
- Dry-run Safety: Simulates all changes before applying them, preventing unintended side effects.
- Branch-based Implementation: Creates a dedicated implementation branch and preserves the mainline.
- Plan-driven Execution: Executes a YAML-defined implementation plan including file creates/modifications, command updates, and agent creation.
- Validation-first Rollback: Runs tests and validation; automatically rolls back if failures occur and opens human reviews via PRs.
- Use Case: As a development team, you can auto-apply high-confidence improvements from /meta-learn in a reversible, auditable way.
Quick Start
Run: 1) Inspect high-confidence suggestions with /meta-learn. 2) Dry-run the implementation with /meta-implement meta-learn-<id> --dry-run. 3) Apply changes with /meta-implement meta-learn-<id> after successful dry-run.