Individual skill

skill-optimizer

Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.

skills/skill-optimizer/SKILL.md

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Matches the observed repository HEAD: Yes
Scanner rules are current: Yes
Repository scan complete: No
Skill scan complete: Yes
Observed behavior: Instructions only
Last checked: Nov 9, 2026

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Repository context

Repository stars (not skill ratings): 1,965
Repository last updated: Oct 27, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

finance Derived category
writing Derived category

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SKILL.mdcore · 2,327 bytes
rules/activation-design.mddoc · 1,263 bytes
rules/benchmark-loop.mddoc · 1,156 bytes
rules/context-budget.mddoc · 1,072 bytes
rules/regression-triage.mddoc · 1,116 bytes
rules/release-gates.mddoc · 963 bytes
tile.jsonasset · 605 bytes

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## When to use

Use this skill when you need to:
- Improve whether a skill is actually applied by models
- Diagnose why some criteria fail across all models
- Prevent a skill from making outputs worse
- Refactor skill text for stronger retrieval under context pressure
- Build repeatable benchmark loops and release gates

## Optimization loop (default workflow)

1. **Measure baseline and skill-on behavior** (per model, per scenario, per criterion)
2. **Find failure pattern**:
   - universal failure (0% with skill)