Agent skill

Skill Quality Reviewer

by Galaxy-Dawn in Galaxy-Dawn/claude-scholar

Scores a skill across description, content organization, writing style and structure, then produces letter grades and a prioritized improvement plan.

MITAuto-check passedAgent Workflows

Install Skill Quality Reviewer

skills CLI
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Galaxy-Dawn/claude-scholar skill-quality-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholar.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-quality-reviewer .claude/skills/skill-quality-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-quality-reviewer
GitHub stars
5.7k
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
841 words
Files
7 (incl. scripts, references)
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Scores a skill across description, content organization, writing style and structure, then produces letter grades and a prioritized improvement plan.

  • Works in 8 steps: Load the Skill → Parse YAML Frontmatter → Evaluate Description Quality (25%) → …
  • Checking a skill's quality before sharing or publishing it
  • SKILL.md covers Overview, When to Use This Skill, Review Modes and Analysis Workflow, plus 4 more sections
  • Runs Shell and Python scripts from its folder

What it does

This meta-skill evaluates other skills along four weighted dimensions: description quality at 25 percent, content organization at 30, writing style at 20 and structural integrity at 25. It loads the skill directory, checks that SKILL.md exists, parses the YAML frontmatter for the required name and description fields and for syntax problems, and then scores each dimension with point-based criteria, such as clear trigger phrases and third-person wording in the description.

Three review modes fit different tasks: score-only for a fast first pass on one skill, remediation-backlog for turning findings into P0, P1 and P2 fix queues with evidence, and batch-portfolio for auditing many skills and clustering repeated issues. Bundled helpers include a skill-audit Python script, a YAML extraction shell script, scoring criteria, good and bad examples and a batch review template.

When your agent uses it

  • Checking a skill's quality before sharing or publishing it
  • Getting a prioritized fix list for an existing skill
  • Auditing a folder of many skills and finding common problems

Example prompts

  • “Analyze skill quality for ./my-skill and give me letter grades.”
  • “Review the skills under ~/.claude/skills and list the biggest repeated issues.”
  • “What should I fix first in my git-workflow skill?”

Requirements

  • Python 3 and a shell for the audit scripts

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Load the Skill
  2. Parse YAML Frontmatter
  3. Evaluate Description Quality (25%)
  4. Evaluate Content Organization (30%)
  5. Evaluate Writing Style (20%)
  6. Evaluate Structural Integrity (25%)
  7. Calculate Weighted Score
  8. Generate Reports

What it can do on your machine

Read from SKILL.md and the folder at commit 9037873. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Shell and Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Quality Reviewer loads about 3k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 76 tokens; SKILL.md has 841 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Galaxy-Dawn/claude-scholar at commit 9037873, republished under its MIT licence (© Galaxy-Dawn). 841 words, ~2,994 tokens.

Download SKILL.mdSave it as .claude/skills/skill-quality-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
skill-quality-reviewer
description
This skill should be used when the user asks to "analyze skill quality", "evaluate this skill", "review skill quality", "check my skill", or "generate quality report". Evaluates local skills across description quality, content organization, writing style, and structural integrity.
version
0.1.0

Skill Quality Reviewer

Overview

A meta-skill for evaluating the quality of Claude Skills. Perform comprehensive analysis across four key dimensions—description quality (25%), content organization (30%), writing style (20%), and structural integrity (25%)—to generate weighted scores, letter grades, and actionable improvement plans.

Use this skill to validate skills before sharing, identify improvement opportunities, or ensure compliance with skill development best practices.

When to Use This Skill

Invoke this skill when:

  • Analyzing a skill's quality before distribution
  • Reviewing skill documentation for best practices
  • Evaluating adherence to skill development standards
  • Generating improvement recommendations for existing skills
  • Validating skill structure and completeness

Trigger phrases:

  • "Analyze skill quality for ./my-skill"
  • "Evaluate this skill: ~/.claude/skills/api-helper"
  • "Review skill quality of git-workflow"
  • "Check my skill for best practices"
  • "Generate quality report for this skill"

Review Modes

Use one of three review modes depending on the task:

  1. score-only
    • fast first-pass grading for one skill.
  2. remediation-backlog
    • convert findings into P0 / P1 / P2 fix queues with concrete evidence.
  3. batch-portfolio
    • review multiple skills together, cluster repeated issues, and produce a prioritized shortlist.

Prefer remediation-backlog when the user asks what to fix next. Prefer batch-portfolio when auditing many skills at once.

Analysis Workflow

Step 1: Load the Skill

Accept skill path as input. Verify the path exists and contains SKILL.md. Read the complete skill directory structure.

bash
# Example invocation
ls -la ~/.claude/skills/target-skill/

Validate:

  • SKILL.md exists
  • Directory is readable
  • Path points to a valid skill
Step 2: Parse YAML Frontmatter

Extract and validate the YAML frontmatter from SKILL.md.

Required fields:

  • name - Skill identifier
  • description - Trigger description with phrases

Check for:

  • Valid YAML syntax
  • No prohibited fields
  • Proper formatting
Step 3: Evaluate Description Quality (25%)

Assess the quality and effectiveness of the frontmatter description.

Scoring breakdown:

CriterionPointsEvaluation
Trigger phrases clarity253-5 specific user phrases present
Third-person format25Uses "This skill should be used when..."
Description length25100-300 characters optimal
Specific scenarios25Concrete use cases, not vague

Red flags:

  • Vague triggers like "helps with tasks"
  • Second-person descriptions ("Use this when you...")
  • Missing or generic descriptions
  • No actionable trigger phrases

Reference: references/examples-good.md for exemplary descriptions

Step 4: Evaluate Content Organization (30%)

Assess adherence to progressive disclosure principles.

Scoring breakdown:

CriterionPointsEvaluation
Progressive disclosure30SKILL.md lean, details in references/
SKILL.md length25Under 5,000 words (1,500-2,000 ideal)
References/ usage25Detailed content properly moved
Logical organization20Clear sections, good flow

Check:

  • SKILL.md body is concise and focused
  • Detailed content moved to references/
  • Examples and templates in appropriate directories
  • No information duplication across files

Reference: references/scoring-criteria.md for detailed rubrics

Step 5: Evaluate Writing Style (20%)

Verify adherence to skill writing conventions.

Scoring breakdown:

CriterionPointsEvaluation
Imperative form40Verb-first instructions throughout
No second person in body30Avoids conversational second person in the main workflow body
Objective language30Factual, instructional tone

Check for:

  • Imperative verbs: "Create the file", "Validate input", "Check structure"
  • Absence of: "You should", "You can", "You need to"
  • Objective, instructional language
  • Consistent style throughout

Good examples:

Create the skill directory structure.
Validate the YAML frontmatter.
Check for required fields.

Bad examples:

You should create the directory.
You need to validate the frontmatter.
Check if the fields are there.
Show full SKILL.md (359 more words)Show less
Step 6: Evaluate Structural Integrity (25%)

Verify the skill's physical structure and completeness.

Scoring breakdown:

CriterionPointsEvaluation
YAML frontmatter30All required fields present
Directory structure30Proper organization
Resource references40All referenced files exist

Validate:

  • YAML frontmatter contains name and description
  • Directory structure follows conventions:
    skill-name/
    ├── SKILL.md
    ├── references/ (optional)
    ├── examples/ (optional)
    └── scripts/ (optional)
  • All files referenced in SKILL.md actually exist
  • Examples are complete and working
  • Scripts are executable
Step 7: Calculate Weighted Score

Compute the overall quality score using weighted dimensions.

Formula:

Overall Score = (Description × 0.25) + (Organization × 0.30) +
                (Style × 0.20) + (Structure × 0.25)

Letter grade mapping:

Score RangeGradeMeaning
97-100A+Exemplary
93-96AExcellent
90-92A-Very Good
87-89B+Good
83-86BAbove Average
80-82B-Solid
77-79C+Acceptable
73-76CSatisfactory
70-72C-Minimal Acceptable
67-69D+Below Standard
63-66DPoor
60-62D-Very Poor
0-59FFail
Step 8: Generate Reports

Create two output documents in the current working directory.

1. Quality Report (quality-report-{skill-name}.md)

  • Executive summary with overall score and grade
  • Dimension-by-dimension breakdown
  • Strengths and weaknesses for each dimension
  • Grade breakdown table
  • Link to improvement plan

2. Improvement Plan (improvement-plan-{skill-name}.md)

  • Prioritized improvement list (High/Medium/Low)
  • Specific file locations and line numbers for issues
  • Current vs. suggested content comparisons
  • Estimated impact on scores
  • Time estimates for fixes
  • Expected score improvement

Output Templates

Quality Report Template
markdown
# Skill Quality Report: {skill-name}

## Executive Summary
- **Overall Score**: X/100 ({Grade})
- **Evaluated**: {Date}
- **Skill Path**: {path}

## Dimension Scores

### 1. Description Quality (25%)
**Score**: X/100

**Strengths**:
- ✅ {specific strength}

**Weaknesses**:
- ❌ {specific weakness}

**Recommendations**:
1. {actionable recommendation}

[Repeat for other dimensions...]

## Grade Breakdown
| Dimension | Score | Weight | Contribution |
|-----------|-------|--------|--------------|
| Description | X/100 | 25% | X.X |
| Organization | X/100 | 30% | X.X |
| Style | X/100 | 20% | X.X |
| Structure | X/100 | 25% | X.X |
| **Overall** | **X/100** | **100%** | **X.X ({Grade})** |

## Next Steps
See `improvement-plan-{skill-name}.md` for detailed improvement suggestions.
Improvement Plan Template
markdown
# Skill Improvement Plan: {skill-name}

## Priority Summary
- **High Priority**: {count} items
- **Medium Priority**: {count} items
- **Low Priority**: {count} items

## High Priority Improvements

### 1. [Issue Title]
**File**: SKILL.md:line:line
**Dimension**: Description Quality
**Impact**: +X points

**Current**:
```yaml
{current content}

Suggested:

yaml
{suggested content}

Reason: {why this improves quality}

[Continue with all issues...]

Quick Wins (Easy Fixes)

  1. {quick fix}
  2. {quick fix}

Estimated Time to Complete

  • High Priority: X hours
  • Medium Priority: X hours
  • Low Priority: X hours
  • Total: X hours

Expected Score Improvement

  • Current: X/100 ({Grade})
  • After High Priority: X/100 ({Grade})
  • After All: X/100 ({Grade})

## Additional Resources

### Reference Files

For detailed evaluation criteria and examples, consult:

- **`references/scoring-criteria.md`** - Comprehensive scoring rubrics for each dimension
- **`references/examples-good.md`** - Exemplary skills demonstrating best practices
- **`references/examples-bad.md`** - Common anti-patterns to avoid

### Scripts

- **`scripts/extract-yaml.sh`** - Utility for extracting YAML frontmatter from SKILL.md
- **`scripts/skill-audit.py`** - Lightweight integrity audit for missing references, word count, and sibling-path checks

### Related Skills

- **`skill-development`** - Comprehensive guide for creating skills
- **`code-review-excellence`** - Best practices for code review

## Best Practices

### When Analyzing Skills

1. **Be objective and specific** - Base scores on observable criteria, not opinions
2. **Provide actionable feedback** - Each recommendation should be concrete and implementable
3. **Include examples** - Show current vs. suggested content for clarity
4. **Estimate impact** - Help users understand which changes matter most
5. **Be constructive** - Frame feedback as opportunities for improvement

### Common Quality Issues

**Description Quality:**
- Vague or generic trigger phrases
- Second-person descriptions
- Missing concrete use cases

**Content Organization:**
- SKILL.md too long (>5,000 words)
- Detailed content not moved to references/
- Poor information hierarchy

**Writing Style:**
- Second-person language ("you", "your")
- Mixed imperative and descriptive styles
- Subjective or conversational tone

**Structural Integrity:**
- Missing required YAML fields
- Referenced files don't exist
- Incomplete examples or broken scripts

### Grade Benchmarks

**A grade (90-100)**: Exemplary skills serving as templates for others
- All dimensions score 85+
- Clear, specific descriptions
- Excellent progressive disclosure
- Consistent imperative style
- Complete, well-organized structure

**B grade (80-89)**: High-quality skills with minor improvements needed
- Most dimensions score 75+
- Good descriptions and organization
- Generally follows best practices
- May have minor style inconsistencies

**C grade (70-79)**: Acceptable skills requiring moderate improvements
- Key areas meet minimum standards
- Some weaknesses in organization or style
- Functional but not exemplary

**D/F grade (below 70)**: Skills needing significant work
- Multiple dimensions below 70
- Major structural or style issues
- Requires comprehensive revision

## Usage Examples

**Example 1: Analyze a local skill**

User: "Analyze skill quality for ~/.claude/skills/git-workflow"

[Claude executes the 8-step workflow and generates:]

  • quality-report-git-workflow.md
  • improvement-plan-git-workflow.md

**Example 2: Review before sharing**

User: "Review my new skill before I publish it"

[Claude analyzes the skill and provides:]

  • Detailed quality assessment
  • Specific improvement recommendations
  • Expected score after implementing fixes

**Example 3: Quality check for existing skill**

User: "Check skill quality of api-helper"

[Claude evaluates and reports:]

  • Current grade and score
  • Top improvement opportunities
  • Quick wins for easy score gains

**Example 4: Batch portfolio review**

User: "Review all skills in ~/.claude/skills and tell me what to fix first"

[Claude evaluates and reports:]

  • portfolio matrix
  • grouped issue clusters
  • shortlist for second-pass remediation

© Galaxy-Dawn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in skills/skill-quality-reviewer of Galaxy-Dawn/claude-scholar.

  • SKILL.md
  • references/batch-review-template.md
  • references/examples-bad.md
  • references/examples-good.md
  • references/scoring-criteria.md
  • scripts/extract-yaml.sh
  • scripts/skill-audit.py

Open the folder on GitHubat commit 9037873

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Galaxy-Dawn/claude-scholar, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Skill Quality Reviewer

What does Skill Quality Reviewer do?

Scores a skill across description, content organization, writing style and structure, then produces letter grades and a prioritized improvement plan. This meta-skill evaluates other skills along four weighted dimensions: description quality at 25 percent, content organization at 30, writing style at 20 and structural integrity at 25.md exists, parses the YAML frontmatter for the required name and description fields and for syntax problems, and then scores each dimension with point-based criteria, such as clear trigger phrases and third-person wording in the description.

When should I use Skill Quality Reviewer?

Skill Quality Reviewer fits situations like: checking a skill's quality before sharing or publishing it; getting a prioritized fix list for an existing skill; auditing a folder of many skills and finding common problems.

How do I install Skill Quality Reviewer in Claude Code?

Run `npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a claude-code`. Or copy the skill folder (skills/skill-quality-reviewer in Galaxy-Dawn/claude-scholar) into .claude/skills/skill-quality-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Skill Quality Reviewer in Codex?

Run `npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a codex`. Or copy the skill folder (skills/skill-quality-reviewer in Galaxy-Dawn/claude-scholar) into .agents/skills/skill-quality-reviewer in your project. Codex loads it when a task matches its description.

Can I use Skill Quality Reviewer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-quality-reviewer, .gemini/skills/skill-quality-reviewer, .github/skills/skill-quality-reviewer and .opencode/skills/skill-quality-reviewer in your project.

What does Skill Quality Reviewer need to run?

Going by SKILL.md and its folder, Skill Quality Reviewer needs a shell and Python for the scripts in its folder. Our summary lists: Python 3 and a shell for the audit scripts.

Does Skill Quality Reviewer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Skill Quality Reviewer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Skill Quality Reviewer use?

Skill Quality Reviewer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Quality Reviewer use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.2k tokens, read only when the agent opens those files.

What are the alternatives to Skill Quality Reviewer?

Skills that share tags, products or a category with Skill Quality Reviewer: Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars), Open-Science Skill Creator (aipoch/open-science, 5.5k stars), OpenCode Skill Creator (antongulin/opencode-skill-creator, 171 stars) and Skill Release Gate (rohitg00/ai-engineering-from-scratch, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Quality Reviewer?

Galaxy-Dawn (a GitHub user) maintains it in Galaxy-Dawn/claude-scholar, which has 5,725 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on September 23, 2026.

Source: Galaxy-Dawn/claude-scholar on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.