Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
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 t...
instructions.md
--- name: skill-optimizer-019de9dea8f6 description: "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." metadata: tags: skills, optimization, benchmarking, activation, regressions, prompt-engineering --- ## 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) - model-specific weakness - regression (negative delta) 3. **Edit for salience**: - add explicit triggers - add concrete integrated examples - tighten checklists and decision rules 4. **Re-run evals** and compare deltas 5. **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](rules/benchmark-loop.md) - End-to-end benchmark loop and scoring - [rules/activation-design.md](rules/activation-design.md) - Improve retrieval and instruction uptake - [rules/context-budget.md](rules/context-budget.md) - Reduce token cost without losing behavior - [rules/regression-triage.md](rules/regression-triage.md) - Diagnose and fix skill-on regressions - [rules/release-gates.md](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--- name: skill-optimizer-019de9dea8f6 description: "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." metadata: tags: skills, optimization, benchmarking, activation, regressions, prompt-engineering --- ## 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) - model-specific weakness - regression (negative delta) 3. **Edit for salience**: - add explicit triggers - add concrete integrated examples - tighten checklists and decision rules 4. **Re-run evals** and compare deltas 5. **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](rules/benchmark-loop.md) - End-to-end benchmark loop and scoring - [rules/activation-design.md](rules/activation-design.md) - Improve retrieval and instruction uptake - [rules/context-budget.md](rules/context-budget.md) - Reduce token cost without losing behavior - [rules/regression-triage.md](rules/regression-triage.md) - Diagnose and fix skill-on regressions - [rules/release-gates.md](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
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.