SKILL.md Optimizer
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Please install @redfox-data/optimize-skill-md according to https://skillhub.cn/install/skillhub.md.
About this skill
Problem
Many SKILL.md files read like project notes rather than Agent instructions: the YAML description lacks WHAT, WHEN, and trigger words; sections mix installation, FAQs, and background explanations; terms and path styles drift; long files dilute the rules an Agent needs to follow. This makes matching less stable and maintenance harder.
How it works
The skill optimizes the document against a standard format:
- YAML repair: rewrite in third person, add
WHAT + WHEN, append trigger words, and keep the field under 1024 characters. - Structure cleanup: require a concise intro and a feature table at the start, add an auth section if missing, and remove empty or user-facing sections.
- Reduction: delete repeated explanations, basic tutorials, and excess examples while preserving executable rules.
- Consistency: unify terms, code-block languages, forward-slash paths, and table style; move oversized details into
references/when needed.
Boundaries
It edits only SKILL.md and does not change README.md, scripts, or referenced files. The goal is to improve expression and structure, not alter behavior. If auth details or references are absent, it should not invent them; a human should verify after optimization.
Use Cases
- Before publishing an Agent Skill, standardize the `SKILL.md` YAML description, intro, feature table, and auth section.
- While maintaining a Skill, remove README-style installation notes, FAQs, and background explanations, keeping only core execution flow.
- When `SKILL.md` exceeds 500 lines, move API parameters, interaction trees, and field mappings into `references/` with inline links.
- Unify terminology, code-block languages, forward-slash paths, and table structure across multiple Skill documents to reduce parsing ambiguity.
Best For
- Independent developers preparing an Agent Skill for publication
- Technical leads maintaining multiple Skill documents and standardizing format
- AI application engineers converting project notes into executable Agent instructions
- Technical writers reviewing YAML, section structure, and auth completeness before release
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