Orchestrate hypothesis-driven skill package improvements with frozen evaluators and rollback.
npx skills add https://github.com/ginmp8/rhapsodia --skill skill-improver-ginmp8
Please help me install this Agent Skill. Skill: skill-improver Source: https://github.com/ginmp8/rhapsodia/tree/main/skills/skill-improver Command: npx skills add https://github.com/ginmp8/rhapsodia --skill skill-improver-ginmp8
Inspect and improve existing skill packages through bounded experiments: freeze an evaluator, measure baseline, discover or load a hypothesis, apply a minimal patch, re-evaluate, and report with rollback safety.
Provide the target skill folder and a frozen evaluator, then run the improvement loop to test a bounded hypothesis.
High-intent search queries and answers about installing and using this skill.
Hypothesis-driven skill improvement orchestrates bounded experiments on existing skill packages by freezing an evaluator, measuring a baseline, applying a minimal patch, and re-evaluating to validate changes with rollback safety.
To safely apply patches to a skill package, provide the target skill folder and a frozen evaluator to run the improvement loop, which enforces change gates, validates packaging, and ensures rollback safety before finalization.
Yes, you can audit and benchmark skill packages safely by freezing evaluator inputs and enforcing change gates, which validate minimal patches and provide a descriptive patch record for rollback if needed.
You need a target skill folder and a frozen evaluator to run the skill improvement loop, which then measures the baseline, discovers or loads a hypothesis, and applies a minimal patch for validation.
Yes, the skill improvement process supports rollback for failed validation by enforcing change gates and validating packaging before finalization, ensuring safe reversion if the evaluator rejects the patch.
A frozen evaluator ensures safe skill validation by locking inputs to measure the baseline and re-evaluate minimal patches consistently, preventing evaluator drift during the bounded improvement experiment.
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