
Skill Review
- 1k installs
- 3.8k repo stars
- Updated May 11, 2026
- sanyuan0704/sanyuan-skills
Skill Review is a Claude Code skill that delivers specific, actionable feedback on structure, clarity, and token efficiency for custom Claude Code skills developers create before publishing.
About
Skill Review is a quality audit skill from sanyuan0704/sanyuan-skills for Claude Code skill authors. It analyzes skill structure, description quality, workflow design, token efficiency, and anti-patterns against documented best practices. The iron law requires every critique to state exactly what to change and why it matters for model behavior—never vague "could be improved" feedback. Trigger phrases include review skill, audit skill, skill quality, check my skill, evaluate skill, skill lint, validate skill, and improve this skill. Developers reach for Skill Review after drafting a SKILL.md and before sharing it with a team or publishing to a skill catalog, when they need a structured pass that catches weak descriptions, bloated prompts, and workflow gaps.
- Follows a strict 3-step workflow: Load Target, Analyze, Report
- Evaluates five core dimensions: structure compliance, description quality, workflow design, token efficiency, and anti-p
- Delivers prioritized, concrete suggestions instead of vague advice
- Always explains exactly what to change and why it improves model output quality
- Accepts explicit path, current directory, or skill name as input
Skill Review by the numbers
- 1,012 all-time installs (skills.sh)
- +39 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #1,042 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/sanyuan0704/sanyuan-skills --skill skill-reviewAdd your badge
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| Installs | 1k |
|---|---|
| repo stars | ★ 3.8k |
| Last updated | May 11, 2026 |
| Repository | sanyuan0704/sanyuan-skills ↗ |
How do you review Claude Code skill quality?
Receive specific, actionable feedback that improves the structure, clarity, and token efficiency of any custom Claude Code skill they create.
Who is it for?
Developers authoring custom Claude Code skills who want a structured quality pass with concrete edits before publishing or sharing.
Skip if: Teams reviewing general application code, pull requests, or non-skill documentation unrelated to SKILL.md agent instruction files.
When should I use this skill?
User says review skill, audit skill, skill quality, check my skill, evaluate skill, skill lint, validate skill, or improve this skill.
What you get
Specific, actionable revision notes covering skill structure, triggers, workflow steps, and token-optimized prompt edits.
- actionable skill revision checklist
- token-optimized prompt edits
Files
Skill Review
IRON LAW: Be specific and actionable. Never say "could be improved" without stating exactly what to change and why it matters for model output quality.
Workflow
Skill Review Progress:
- [ ] Step 1: Load Target ⚠️ REQUIRED
- [ ] 1.1 Identify skill path
- [ ] 1.2 Read SKILL.md and inventory all files
- [ ] Step 2: Analyze ⚠️ REQUIRED
- [ ] 2.1 Structure compliance
- [ ] 2.2 Description quality
- [ ] 2.3 Workflow design
- [ ] 2.4 Token efficiency
- [ ] 2.5 Anti-pattern detection
- [ ] Step 3: Report ⚠️ REQUIRED
- [ ] 3.1 Strengths (what's done well)
- [ ] 3.2 Suggestions (prioritized improvements)Step 1: Load Target ⚠️ REQUIRED
Identify the skill to review. Accept:
- Explicit path:
/skill-review path/to/skill - Current directory context: if user is already in a skill folder
- Skill name: search within the workspace for matching skill directory
Read the full SKILL.md and list all files in the skill directory. Count SKILL.md line count — this is a key metric.
Step 2: Analyze ⚠️ REQUIRED
Load references/review-criteria.md for detailed criteria. Evaluate the skill across five dimensions:
2.1 Structure Compliance
Questions to answer:
- Does the directory follow the standard layout (SKILL.md, scripts/, references/, assets/)?
- Is SKILL.md under 500 lines?
- Does frontmatter contain only
nameanddescription(plus optionalallowed-tools,license,metadata)? - Are there unnecessary files (README.md, CHANGELOG.md, LICENSE duplicates)?
- Are references organized by domain with one level of nesting?
2.2 Description Quality
Questions to answer:
- Does the description include concrete trigger keywords and phrases?
- Does it use keyword bombing (multiple phrasings of the same intent)?
- Is it self-contained — can a router understand what this skill does without reading the body?
- Does it avoid putting "When to Use" info in the body instead of the description?
- Would a user's natural language query match this description?
2.3 Workflow Design
Questions to answer:
- Is there a trackable checklist with copy-paste-friendly format?
- Are critical steps marked with ⚠️ REQUIRED or ⛔ BLOCKING?
- Are there confirmation gates before destructive/generative operations?
- Is the workflow linear and progressive, or does it jump around?
- Are sub-steps used where complexity demands it?
2.4 Token Efficiency
Questions to answer:
- Is there an Iron Law or core constraint at the top?
- Does SKILL.md only contain what Claude doesn't already know?
- Are references loaded progressively (on-demand) rather than all upfront?
- Are instructions in imperative form (not "You should...")?
- Are scripts executed rather than loaded into context?
- Is there redundancy between SKILL.md and reference files?
2.5 Anti-Pattern Detection
Check for these known bad patterns:
- Vague directives ("ensure good quality", "make it better")
- Placeholder residue (TODO, FIXME, xxx, TBD)
- Over-specification of things Claude already knows
- No anti-patterns section (model has no guardrails against lazy defaults)
- Missing pre-delivery checklist (no concrete verification criteria)
- Giant monolithic SKILL.md with no reference extraction
- Instructions that describe WHAT rather than constrain HOW
Step 3: Report ⚠️ REQUIRED
Output Format
Present the review in this order:
1. Strengths — What this skill does well. Be specific: quote the actual lines or patterns that work. Minimum 2 strengths, even for weak skills (find what's salvageable).
2. Suggestions — Improvements sorted by impact (highest first). Each suggestion must include:
- What: the specific issue found
- Where: file and location
- Fix: concrete actionable change (show before/after when helpful)
Group suggestions by dimension only if there are many (5+). Otherwise present as a flat prioritized list.
Tone
- Direct, constructive, collegial
- Lead with genuine strengths — not filler praise
- Suggestions are opportunities, not failures
- If the skill is already solid, say so briefly and move on
Anti-Patterns for This Skill
- Giving vague praise ("nice structure!") without quoting what specifically works
- Listing problems without actionable fixes
- Reviewing against personal taste rather than the documented principles
- Suggesting over-engineering for simple skills
- Flagging missing features that the skill intentionally omits (check if simplicity is the point)
Review Criteria Reference
Detailed criteria for each review dimension. Load this file during Step 2 analysis.
Structure Compliance — Detailed Checks
Required
SKILL.mdexists at root- Frontmatter has
namefield (matches directory name) - Frontmatter has
descriptionfield (non-empty, >50 chars)
Expected
SKILL.md≤ 500 lines (hard limit; flag if >400 as approaching)- No files that serve no purpose: README.md (unless skill IS about READMEs), CHANGELOG.md, CONTRIBUTING.md
scripts/— if present, all files are executable and testedreferences/— if present, each file is referenced from SKILL.md with load instructionsassets/— if present, files are used in output, not loaded into context
Red Flags
- SKILL.md > 500 lines → must extract to references
- Deeply nested references (2+ levels) → flatten
- Orphaned files (not referenced from SKILL.md)
- Scripts without clear invocation instructions in SKILL.md
Description Quality — Detailed Checks
Keyword Bombing Indicators (Good)
- 3+ verb variations for the same action (create/build/write/make)
- Noun variations (skill/agent/tool/plugin)
- Natural phrases a user would type
- Both imperative ("create a skill") and question form ("how do I build a skill")
Description Anti-Patterns (Bad)
- Single-sentence description under 100 characters
- Only technical terms, no natural language triggers
- Describes implementation ("Uses markdown frontmatter...") instead of user intent
- Contains instructions meant for the model body
Scoring Heuristic
- Count unique trigger phrases → <3 is weak, 5-8 is good, >10 is excellent
- Check if casual user language would match → try 3 hypothetical queries mentally
Workflow Design — Detailed Checks
Checklist Quality
- Uses
- [ ]format (copy-paste friendly) - Steps are numbered and sequential
- Sub-steps use indentation
- ⚠️ marks appear on steps that must not be skipped
- ⛔ marks appear on prerequisites/blockers
Confirmation Gates
Must exist before:
- File creation/deletion/overwrite
- External API calls with side effects
- Long-running generative operations
- Applying analysis results to code
Flow Quality
- Can a reader understand the full process from the checklist alone?
- Are conditional branches clearly marked?
- Is there a clear start and end state?
Token Efficiency — Detailed Checks
Iron Law
- Present: yes/no
- Placed at top (before workflow): yes/no
- Specific and falsifiable: yes/no (bad: "write good code"; good: "never use placeholder text")
- Addresses the #1 likely failure mode: yes/no
Progressive Loading Signals (Good)
- "Load references/X.md for..." with clear trigger conditions
- References loaded at specific workflow steps, not all in Step 1
- Large context only loaded when that branch is taken
Bloat Signals (Bad)
- Explaining things Claude knows (how to write markdown, what JSON is)
- "You should" / "You will" / "Remember to" (wasted tokens on filler)
- Duplicated instructions (same thing said in workflow AND in later section)
- Comments/explanations aimed at human readers, not model behavior
Anti-Pattern Detection — Detailed Examples
Vague Directives (rewrite as questions)
- ❌ "Ensure the output is high quality"
- ✅ "Does every heading have ≥2 sentences of content beneath it?"
Over-Specification
- ❌ "Use markdown headers with # for h1, ## for h2..." (Claude knows this)
- ✅ "Use exactly 2 levels of headers: # for sections, ## for subsections" (this constrains)
Missing Guardrails
- No anti-patterns section = model will take lazy defaults
- No pre-delivery checklist = no verification step
- No Iron Law = no north star for quality decisions
Monolithic SKILL.md
- If SKILL.md > 300 lines, check: could any section be a reference loaded on-demand?
- Domain knowledge that applies to only one step → extract to reference
- Examples longer than 10 lines → extract to reference
Related skills
FAQ
What does Skill Review analyze in a Claude Code skill?
Skill Review analyzes skill structure, description quality, workflow design, token efficiency, and anti-patterns against best practices. Every finding must state exactly what to change and why it matters for model behavior.
When should developers run Skill Review?
Developers should run Skill Review when they want to review, audit, lint, evaluate, validate, or improve a custom Claude Code skill. Common triggers include phrases like review skill, audit skill, check my skill, or is this skill good.