skill-iter-tune
SolidIterative skill tuning via execute-evaluate-improve feedback loop. Uses maestro delegate Claude to execute skill, Agy to evaluate quality, and Agent to apply improvements. Iterates until quality threshold or max iterations. Triggers on "skill iter tune", "iterative skill tuning", "tune skill".
Install
Quality Score: 87/100
Skill Content
Details
- Author
- catlog22
- Repository
- catlog22/maestro-flow
- Created
- 4 months ago
- Last Updated
- today
- Language
- TypeScript
- License
- None
Similar Skills
Semantically similar based on skill content — not just same category
skill-tuning
Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Agy CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".
tune
Live RL tuner for skills. Watches skill invocations, reads user reaction, proposes targeted SKILL.md overlay edits, requires explicit approval, writes scorecards. The in-session half of the skill-RL loop (Path B). Triggers on: tune, sharpen, skill feedback, that was shit, that was great, make X better.
skill-tuning-loop
Empirically test whether a lacquer skill needs an edit by mining real session transcripts for friction, proposing a bounded fix, and validating it against held-out cases before it ships. Use when a skill in core/skills/ or profiles/*/skills/ keeps getting corrected in practice, when triaging a scheduled tuning-pass PR, or when skill-authoring-standard's manual rubric isn't enough to tell whether a proposed edit actually helps. Distinct from skill-authoring-standard (a static prose rubric checked by a human) — this is an empirical, evidence-gated loop checked by rollouts.