agent-identifier
Use when creating or configuring Claude Code agents and their frontmatter.
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
Claude Code
$ npx -y skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer --agent claude-code
Codex
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a codex -y
Cursor
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a cursor -y
Gemini CLI
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a gemini-cli -y
GitHub Copilot
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a github-copilot -y
OpenCode
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a opencode -y
Windsurf
$ npx skills add Galaxy-Dawn/claude-scholar --skill skill-quality-reviewer -a windsurf -y
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
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.
**Invoke this skill when:**
**Trigger phrases:**
Use one of three review modes depending on the task:
1. **score-only**
2. **remediation-backlog**
3. **batch-portfolio**
Prefer `remediation-backlog` when the user asks what to fix next. Prefer `batch-portfolio` when auditing many skills at once.
Accept skill path as input. Verify the path exists and contains `SKILL.md`. Read the complete skill directory structure.
# Example invocation ls -la ~/.claude/skills/target-skill/
**Validate:**
Extract and validate the YAML frontmatter from SKILL.md.
**Required fields:**
**Check for:**
Assess the quality and effectiveness of the frontmatter description.
**Scoring breakdown:**
| Criterion | Points | Evaluation | |-----------|--------|------------| | Trigger phrases clarity | 25 | 3-5 specific user phrases present | | Third-person format | 25 | Uses "This skill should be used when..." | | Description length | 25 | 100-300 characters optimal | | Specific scenarios | 25 | Concrete use cases, not vague |
**Red flags:**
**Reference:** `references/examples-good.md` for exemplary descriptions
Assess adherence to progressive disclosure principles.
**Scoring breakdown:**
| Criterion | Points | Evaluation | |-----------|--------|------------| | Progressive disclosure | 30 | SKILL.md lean, details in references/ | | SKILL.md length | 25 | Under 5,000 words (1,500-2,000 ideal) | | References/ usage | 25 | Detailed content properly moved | | Logical organization | 20 | Clear sections, good flow |
**Check:**
**Reference:** `references/scoring-criteria.md` for detailed rubrics
Verify adherence to skill writing conventions.
**Scoring breakdown:**
| Criterion | Points | Evaluation | |-----------|--------|------------| | Imperative form | 40 | Verb-first instructions throughout | | No second person in body | 30 | Avoids conversational second person in the main workflow body | | Objective language | 30 | Factual, instructional tone |
**Check for:**
**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.
Verify the skill's physical structure and completeness.
**Scoring breakdown:**
| Criterion | Points | Evaluation | |-----------|--------|------------| | YAML frontmatter | 30 | All required fields present | | Directory structure | 30 | Proper organization | | Resource references | 40 | All referenced files exist |
**Validate:**
skill-name/ ├── SKILL.md ├── references/ (optional) ├── examples/ (optional) └── scripts/ (optional)
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 Range | Grade | Meaning | |-------------|-------|---------| | 97-100 | A+ | Exemplary | | 93-96 | A | Excellent | | 90-92 | A- | Very Good | | 87-89 | B+ | Good | | 83-86 | B | Above Average | | 80-82 | B- | Solid | | 77-79 |
Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication.
Use when creating or configuring Claude Code agents and their frontmatter.
Use only when creating new registrable ML components that require Factory or Registry patterns.
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