Skill Refiner: Iterative Self-Improvement Loop

Adaptive evaluation loop for AI skill collections, inspired by Karpathy's AutoResearch. Orchestrates repeated score-improve-verify cycles using skill-creator as the engine and mandatory peer review as an adversarial check (cross-model when a secondary harness is available, fresh-context self-review as the minimum fallback).

When to use

• Batch-improving the entire skill collection after a period of manual edits
• Targeted improvement of one named skill when the user explicitly asks for skill-refiner, multiple iterations, a score target, or "no ceiling" polish
• Running quality sweeps before a release or publish
• Triggering a self-improvement cycle where skills bootstrap each other
• After adding several new skills that need polish and consistency alignment
• When cross-model perspective would catch single-model blind spots
• Periodic maintenance: scheduled improvement runs to keep skills current

When NOT to use