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Skillv1.0.0

skill-grader

Evaluates Claude Agent Skills on 10 quality axes with letter grades (A+ through F) and specific improvement recommendations. Use when auditing a skill, comparing skills, prioritizing improvements, or

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Imported from majiayu000/claude-skill-registry-data (skills/skill-grader/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry-data --skill skill-grader. Copyright stays with the author.

Skill Grader

Structured evaluation rubric for Claude Agent Skills. Produces letter grades (A+ through F) on 10 axes plus an overall grade, with specific improvement recommendations for each axis.

Designed for sub-agents and non-expert reviewers who need a mechanical, repeatable process for assessing skill quality without deep domain expertise.


When to Use

Use for:

  • Auditing a single skill's quality
  • Comparing skills against each other
  • Prioritizing which skills to improve first
  • Quality control sweeps across a skill library
  • Generating improvement roadmaps

NOT for:

  • Creating new skills (use skill-architect)
  • Grading code quality or non-skill documents
  • Evaluating agent performance (different from skill quality)

Grading Process

flowchart TD
  A[Read SKILL.md + all files] --> B[Score each of 10 axes]
  B --> C[Assign letter grade per axis]
  C --> D[Compute overall grade]
  D --> E[Write improvement recommendations]
  E --> F[Produce grading report]

Step-by-Step

  1. Read the entire skill folder — SKILL.md, all references, scripts, CHANGELOG, README
  2. Score each axis — Use the rubric below (0-100 per axis)
  3. Convert to letter grade — See grade scale
  4. Compute overall grade — Weighted average (Description and Scope are 2x weight)
  5. Write 1-3 specific improvements per axis scoring below B+
  6. Produce the grading report in the output format below

The 10 Evaluation Axes

Axis 1: Description Quality (Weight: 2x)

Does the description follow [What] [When] [Keywords]. NOT for [Exclusions]?

Grade Criteria
A Specific verb+noun, domain keywords users would type, 2-5 explicit NOT exclusions, 25-50 words
B Has keywords and NOT clause, but slightly vague or missing synonym coverage
C Too generic, missing NOT clause, or >100 words of process detail
D Single vague sentence ("helps with X") or name/description mismatch
F Missing or empty description

Axis 2: Scope Discipline (Weight: 2x)

Is the skill narrowly focused on one expertise type, or a catch-all?

Grade Criteria
A One clear expertise domain, "When to Use" and "NOT for" sections both present and specific
B Mostly focused, minor boundary ambiguity
C Covers 2-3 related but distinct domains, should probably be split
D Catch-all skill ("helps with anything related to X")
F No scope boundaries defined at all

Axis 3: Progressive Disclosure

Does the skill follow the three-layer architecture (metadata → SKILL.md → references)?

Grade Criteria
A SKILL.md <300 lines, heavy content in references, reference index in SKILL.md with 1-line descriptions
B SKILL.md <500 lines, some references used, index present
C SKILL.md >500 lines, or all content inlined with no references
D SKILL.md >800 lines, or references exist but aren't indexed in SKILL.md
F Single massive file with no structure

Axis 4: Anti-Pattern Coverage

Does the skill encode expert knowledge that prevents common mistakes?

Grade Criteria
A 3+ anti-patterns with Novice/Expert/Timeline template, LLM-mistake notes
B 1-2 anti-patterns with clear explanation
C Anti-patterns mentioned but no structured template
D No anti-patterns, just positive instructions
F Contains advice that IS an anti-pattern (outdated, harmful)

Axis 5: Self-Contained Tools

Does the skill include working tools (scripts, MCPs, subagents)?

Grade Criteria
A Working scripts with CLI interface, error handling, dependency docs; OR valid "no tools needed" justification
B Scripts exist and work but lack error handling or docs
C Scripts referenced but are templates/pseudocode
D Phantom tools (SKILL.md references files that don't exist)
F References non-existent tools AND no acknowledgment

Note: Not every skill needs tools. A pure decision-tree skill can score A if tools aren't applicable.

Axis 6: Activation Precision

Would the skill activate correctly on relevant queries and stay silent on irrelevant ones?

Grade Criteria
A Description has specific keywords matching user language, clear NOT clause, no obvious false-positive vectors
B Good keywords, minor false-positive risk
C Generic keywords that overlap with other skills
D No specific keywords, or NOT clause contradicts intended use
F Description would cause constant false activation

Axis 7: Visual Artifacts

Does the skill use Mermaid diagrams, code examples, and tables effectively?

Grade Criteria
A Decision trees as Mermaid flowcharts, tables for comparisons, code examples for concrete patterns
B Some diagrams or tables, but key decision trees still in prose
C Tables used but no Mermaid diagrams for processes
D Prose-only, no visual structure
F Wall of text with no formatting aids

Axis 8: Output Contracts

Does the skill define what it produces in a format consumable by other agents?

Grade Criteria
A Explicit output format (JSON schema, markdown template, or structured sections), subagent-consumable
B Output format implied but not explicitly documented
C No output format, but content is structured enough to infer
D Unstructured prose output expected
F N/A (pure reference skill) — exempt from this axis

Axis 9: Temporal Awareness

Does the skill track when knowledge was current and what has changed?

Grade Criteria
A Timelines in anti-patterns, "as of [date]" markers, CHANGELOG with dates
B Some temporal context, CHANGELOG exists
C No dates on knowledge, but CHANGELOG exists
D No temporal context anywhere, knowledge could be stale
F Contains demonstrably outdated advice without warning

Axis 10: Documentation Quality

README, CHANGELOG, and reference organization.

Grade Criteria
A README with quick start, CHANGELOG with dated versions, references well-organized with clear filenames
B README and CHANGELOG exist, references present
C SKILL.md is the only file, but it's well-structured
D No README, no CHANGELOG, disorganized references
F SKILL.md is the only file and it's poorly structured

Grade Scale

Letter Score Range Meaning
A+ 97-100 Exemplary — sets the standard
A 93-96 Excellent — minor improvements possible
A- 90-92 Very good — a few small gaps
B+ 87-89 Good — notable room for improvement
B 83-86 Solid — several areas need work
B- 80-82 Above average — meaningful gaps
C+ 77-79 Average — significant improvements needed
C 73-76 Below average — major gaps
C- 70-72 Barely adequate
D+ 67-69 Poor — fundamental issues
D 63-66 Very poor — needs major rework
D- 60-62 Near-failing quality
F <60 Failing — start over

Overall Grade Computation

Axes 1 (Description) and 2 (Scope) carry 2x weight. All others carry 1x weight. If Axis 8 (Output Contracts) is marked exempt, remove it from the calculation.

Overall = (2×Axis1 + 2×Axis2 + Axis3 + Axis4 + Axis5 + Axis6 + Axis7 + Axis8 + Axis9 + Axis10) / 12

Convert the numeric average to a letter grade using the scale above.


Output Format

Produce this exact structure:

# Skill Grading Report: [skill-name]

**Graded**: [date]
**Overall Grade**: [letter] ([score]/100)

## Axis Grades

| # | Axis | Grade | Score | Key Finding |
|---|------|-------|-------|-------------|
| 1 | Description Quality | [grade] | [score] | [1-line finding] |
| 2 | Scope Discipline | [grade] | [score] | [1-line finding] |
| 3 | Progressive Disclosure | [grade] | [score] | [1-line finding] |
| 4 | Anti-Pattern Coverage | [grade] | [score] | [1-line finding] |
| 5 | Self-Contained Tools | [grade] | [score] | [1-line finding] |
| 6 | Activation Precision | [grade] | [score] | [1-line finding] |
| 7 | Visual Artifacts | [grade] | [score] | [1-line finding] |
| 8 | Output Contracts | [grade] | [score] | [1-line finding] |
| 9 | Temporal Awareness | [grade] | [score] | [1-line finding] |
| 10 | Documentation Quality | [grade] | [score] | [1-line finding] |

## Top 3 Improvements (Highest Impact)

1. **[Axis]: [Specific action]** — [Why this matters, expected grade improvement]
2. **[Axis]: [Specific action]** — [Why this matters, expected grade improvement]
3. **[Axis]: [Specific action]** — [Why this matters, expected grade improvement]

## Detailed Notes

### [Axis name] ([grade])
[2-3 sentences of specific feedback with examples from the skill]

[Repeat for each axis scoring below B+]

Quick Grading (Abbreviated)

For rapid triage across many skills, produce only:

| Skill | Overall | Desc | Scope | Disc | Anti | Tools | Activ | Visual | Output | Temp | Docs |
|-------|---------|------|-------|------|------|-------|-------|--------|--------|------|------|
| [name] | [grade] | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

Anti-Patterns in Grading

Grade Inflation

Wrong: Giving B+ because "it's pretty good" without checking criteria. Right: Match observations to the rubric table literally. If the description lacks a NOT clause, it cannot score above C on Axis 1.

Missing Context

Wrong: Grading a pure decision-tree skill poorly on Axis 5 (tools) because it has no scripts. Right: Mark Axis 5 as "A — tools not applicable for this skill type."

Ignoring Phantoms

Wrong: Scoring Axis 5 as B because scripts are "referenced." Right: Actually check if every referenced file exists. If scripts/validate.py is mentioned but doesn't exist, that's D.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/majiayu000-claude-skill-registry-data-skill-grader/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

majiayu000-claude-skill-registry-data-skill-grader.ocm.jsonjson
{
  "ocm": "1",
  "id": "majiayu000-claude-skill-registry-data-skill-grader",
  "kind": "skill",
  "name": "skill-grader",
  "description": "Evaluates Claude Agent Skills on 10 quality axes with letter grades (A+ through F) and specific improvement recommendations. Use when auditing a skill, comparing skills, prioritizing improvements, or performing quality control on a skill library. Activate on \"grade skill\", \"evaluate skill\", \"skill quality\", \"skill audit\", \"skill review\", \"rate skill\". NOT for creating skills (use skill-architect), grading code quality, or evaluating non-skill documents.",
  "publisher": "majiayu000",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "coding"
    ],
    "tags": [
      "skill-md",
      "grader",
      "grade-skill",
      "evaluate-skill",
      "skill-quality",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Evaluates Claude Agent Skills on 10 quality axes with letter grades (A+ through F) and specific improvement recommendations. Use when auditing a skill, comparing skills, prioritizing improvements, or performing quality control on a skill library. Activate on \"grade skill\", \"evaluate skill\", \"skill quality\", \"skill audit\", \"skill review\", \"rate skill\". NOT for creating skills (use skill-architect), grading code quality, or evaluating non-skill documents."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/majiayu000/claude-skill-registry-data",
      "path": "skills/skill-grader/SKILL.md",
      "ref": "f863c11651055f498e1466492dc2a0d95721d2d5",
      "url": "https://github.com/majiayu000/claude-skill-registry-data/blob/f863c11651055f498e1466492dc2a0d95721d2d5/skills/skill-grader/SKILL.md",
      "key": "majiayu000/claude-skill-registry-data/skills/skill-grader/SKILL.md"
    },
    "allowed_tools": [
      "Read,Grep,Glob"
    ]
  },
  "instructions": "# Skill Grader\n\nStructured evaluation rubric for Claude Agent Skills. Produces letter grades (A+ through F) on 10 axes plus an overall grade, with specific improvement recommendations for each axis.\n\nDesigned for sub-agents and non-expert reviewers who need a mechanical, repeatable process for assessing skill quality without deep domain expertise.\n\n---\n\n## When to Use\n\n✅ **Use for**:\n- Auditing a single skill's quality\n- Comparing skills against each other\n- Prioritizing which skills to improve first\n- Quality control sweeps across a skill library\n- Generating improvement roadmaps\n\n❌ **NOT for",
  "cost": {
    "context_tokens": 2517
  }
}

Fetch it by URL: GET /api/v1/registry/majiayu000-claude-skill-registry-data-skill-grader/manifest?version=1.0.0

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