skill-optimizer
- Repo stars 1,812
- Author updated Live
- Author repo skills
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @mcollina · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: skill-optimizer
description: Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or e…
category: ai
runtime: no special runtime
---
# skill-optimizer output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to use / Optimization loop (default workflow) / How to use” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “When to use / Optimization loop (default workflow) / How to use” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “When to use / Optimization loop (default workflow) / How to use”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: skill-optimizer
description: Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or e…
category: ai
source: mcollina/skills
---
# skill-optimizer
## When to use
- Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, di…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to use / Optimization loop (default workflow) / How to use” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "skill-optimizer" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to use / Optimization loop (default workflow) / How to use
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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
Decide Fit First
Design Intent
How To Use It
Boundaries And Review