modelscope.cn

optimize-skill

Analyzes and optimizes an existing agent skill for conciseness, discoverability, and adherence to best practices.

Installation

$ npx skills add https://modelscope.cn

Summary

  • Analyzes and optimizes an existing agent skill for conciseness, discoverability, and adherence to best practices.
  • Use when a skill needs improvement, is too verbose, has poor activation rates, or fails to follow progressive disclosure patterns.
  • Do NOT use for creating a new skill from scratch — use create-skill instead.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from modelscope.cn · top by installs.

npx skills add https://modelscope.cn

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseMIT
More metadata
author
github.com/hyf0
version
1.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,465 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Workflow

Follow these 5 steps in order. Copy this checklist into your response and check off each step as you complete it:

Task Progress:
- [ ] Step 1: Read the target skill
- [ ] Step 2: Run the quality checklist
- [ ] Step 3: Identify optimization opportunities
- [ ] Step 4: Apply optimizations
- [ ] Step 5: Validate improvements

Step 1: Read the Target Skill

Read the target skill's SKILL.md and all files in its reference/ directory (if any).

Collect these metrics:

  • Total line count of SKILL.md
  • Total line count across all reference files
  • Frontmatter fields present vs. missing
  • Number of reference files and whether all are linked from SKILL.md

Report these metrics to the user before proceeding.

Step 2: Run the Quality Checklist

Score each item in the quality checklist as PASS, FAIL, or N/A: -> See [quality-checklist](reference/quality-checklist.md)

Present the full scorecard to the user before making any changes. Ask for confirmation to proceed with optimizations.

Step 3: Identify Optimization Opportunities

Review all FAIL items from the checklist. Prioritize by impact (highest first):

  1. Description quality — Most common cause of skill not being invoked. Fix first.
  2. Content compression — Remove knowledge the agent already has. Reduces token cost and noise.
  3. Progressive disclosure — Split oversized SKILL.md into reference files, or merge tiny reference files back.
  4. Structure clarity — Improve headers, cross-references, and flow. Numbered steps for workflows.
  5. Consistency — Fix terminology, formatting, and style inconsistencies.
  6. Triggering precision — Under-triggering: add keywords, trigger phrases, concrete use cases. Over-triggering: add negative triggers ("Do NOT use for X"), narrow the scope.

List each optimization opportunity with:

  • What is wrong
  • Why it matters
  • What the fix will be

Step 4: Apply Optimizations

For each issue identified in Step 3, apply the fix. Use compression techniques where applicable: -> See [compression-techniques](reference/compression-techniques.md)

For each change, briefly explain what changed and why in your response (one line per change is sufficient).

Step 5: Validate Improvements

After applying all optimizations:

  • Re-run the quality checklist — all previously FAIL items should now PASS
  • Verify SKILL.md is under 500 lines
  • Verify no content was lost — all original capabilities are preserved
  • Read the final SKILL.md end-to-end for coherence
  • Verify all reference file links resolve to existing files
  • Triggering test: Ask yourself "When would you use the [skill-name] skill?" — verify the description clearly communicates the skill's purpose and trigger conditions

Report the before/after metrics:

  • Line count: before -> after
  • Checklist score: X/Y PASS -> X/Y PASS
  • Key improvements made

Output

Deliver the optimized skill files to the user with a summary of all changes made and their rationale.