Name: skill-optimizer
Author: mcollina

About

Imported from mcollina/skills (skills/skill-optimizer/SKILL.md) via skills.sh. Install upstream with npx skills add mcollina/skills --skill skill-optimizer. Copyright stays with the author.

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

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/mcollina-skills-skill-optimizer/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

mcollina-skills-skill-optimizer.ocm.jsonjson

{
  "ocm": "1",
  "id": "mcollina-skills-skill-optimizer",
  "kind": "skill",
  "name": "skill-optimizer",
  "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.",
  "publisher": "mcollina",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "finance"
    ],
    "tags": [
      "skill-md",
      "skills",
      "optimization",
      "benchmarking",
      "activation",
      "regressions",
      "prompt-engineering",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/mcollina/skills",
      "path": "skills/skill-optimizer/SKILL.md",
      "ref": "HEAD",
      "url": "https://www.skills.sh/mcollina/skills/skill-optimizer",
      "key": "mcollina/skills/skills/skill-optimizer/SKILL.md"
    }
  },
  "instructions": "## When to use\n\nUse this skill when you need to:\n- Improve whether a skill is actually applied by models\n- Diagnose why some criteria fail across all models\n- Prevent a skill from making outputs worse\n- Refactor skill text for stronger retrieval under context pressure\n- Build repeatable benchmark loops and release gates\n\n## Optimization loop (default workflow)\n\n1. **Measure baseline and skill-on behavior** (per model, per scenario, per criterion)\n2. **Find failure pattern**:\n   - universal failure (0% with skill)\n   - model-specific weakness\n   - regression (negative delta)\n3. **Edit for salie",
  "cost": {
    "context_tokens": 438
  }
}

Fetch it by URL: GET /api/v1/registry/mcollina-skills-skill-optimizer/manifest?version=1.0.0

Reviews

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