skill-optimizer

SKILL.md

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)

Measure baseline and skill-on behavior (per model, per scenario, per criterion)

Find failure pattern:

universal failure (0% with skill)

model-specific weakness

regression (negative delta)

Edit for salience:

add explicit triggers

add concrete integrated examples

tighten checklists and decision rules

Re-run evals and compare deltas

Ship with guardrails (documented gate + run history + follow-up issues)

How to use

Read individual rule files for detailed procedures and templates:

rules/benchmark-loop.md - End-to-end benchmark loop and scoring

rules/activation-design.md - Improve retrieval and instruction uptake

rules/context-budget.md - Reduce token cost without losing behavior

rules/regression-triage.md - Diagnose and fix skill-on regressions

rules/release-gates.md - Go/no-go criteria before shipping skill updates

Practical heuristics

Prefer few high-signal rules over many soft recommendations

Put fragile, high-value behaviors in top-level checklists

Include at least one integrated example per common scenario

Add explicit wording for what must not be omitted

Track gains/losses with with-skill vs without-skill comparisons