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skill-forge-review

skill-forge-review

github.com/agricidanielTesting & quality18637 installsUpdated 29w ago
Repository

About skill-forge-review

A review skill for validating Claude skills, from locating files to checking structure, metadata, triggers, instructions, scripts, and multi-skill architecture. It also guides health scoring and creation of trigger evaluation queries.

Key features

  1. 01Validates skill structure
  2. 02Audits metadata and descriptions
  3. 03Analyzes activation triggers
  4. 04Reviews instructions and scripts
  5. 05Generates trigger evaluation sets
  6. 06Scores skill health

Use cases

  1. 01Checking a skill before sharing
  2. 02Finding activation gaps and false triggers
  3. 03Reviewing a multi-skill setup
  4. 04Preparing trigger tests

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/agricidaniel/skill-forge --skill skill-forge-review

After installing, type/skill-forge-reviewin Claude Code to confirm it loaded.

Details

From the project README

Skill Review & Validation

Process

Step 1: Locate Skill Files

Accept input as:

  • Path to a skill directory
  • Skill name (search in ~/.claude/skills/)
  • URL to a GitHub repository

Read all .md files, scripts, and asset files.

Step 2: Structure Validation

Run python scripts/validate_skill.py <path> for programmatic checks.

Manual verification:

  • SKILL.md exists (exact case)
  • No README.md inside skill folder
  • Folder name matches name field
  • Valid kebab-case naming (1-64 chars)
  • No "claude" or "anthropic" in name
Step 3: Frontmatter Audit
CheckPass Criteria
Name formatkebab-case, 1-64 chars, no leading/trailing hyphens
Description presentNon-empty, 1-1024 characters
Description has WHATExplains capabilities
Description has WHENIncludes trigger phrases
Description has keywordsDomain-specific terms included
No XML tagsNo < or > characters
Optional fields validlicense, compatibility (<500 chars), metadata
Step 4: Triggering Analysis

Assess the description for activation quality:

Under-triggering risks:

  • Too generic ("Helps with projects")
  • Missing common paraphrases
  • No domain keywords
  • Missing file type mentions (if relevant)

Over-triggering risks:

  • Too broad ("Processes documents")
  • Overlaps with built-in Claude capabilities
  • Missing negative triggers for disambiguation

Generate test queries:

  • 5 queries that SHOULD trigger the skill
  • 5 queries that SHOULD NOT trigger
  • 3 edge cases (ambiguous queries)
Step 5: Instruction Quality
CriterionScore (0-10)
SpecificityAre instructions actionable? (not "validate properly")
CompletenessAll workflows covered?
Error handlingCommon failures addressed?
ExamplesConcrete examples provided?
Progressive disclosureDetailed docs in references/ not SKILL.md?
LengthUnder 500 lines / 5000 tokens?
Cross-referencesClear links to references/scripts?
Step 6: Architecture Review (Multi-skill)

For skills with sub-skills:

  • Main skill has clear routing table
  • Sub-skills have focused responsibilities
  • Cross-references are valid (files exist)
  • Naming follows parent-child convention
  • Shared references in parent, not duplicated
  • Agents have clear roles (if Tier 4)
Step 7: Script Quality (if present)
  • Docstrings with purpose, input, output
  • CLI interface (argparse or similar)
  • Structured output (JSON)
  • Error handling (try/except with clear messages)
  • No hardcoded paths or secrets
  • Minimal dependencies
Step 8: Generate Skill Health Score

Scoring methodology (0-100):

CategoryWeightChecks
Frontmatter Quality25%Name, description, format
Trigger Accuracy20%WHAT + WHEN + keywords
Instruction Quality25%Specificity, completeness, examples
Structure Compliance15%File naming, organization, references
Script Quality10%If applicable (full marks if no scripts needed)
Progressive Disclosure5%Proper use of 3-level system
Step 9: Generate Trigger Eval Set

After reviewing, generate a structured trigger eval set for ongoing testing:

  1. Run python scripts/generate_eval_set.py <path> to auto-generate a starter set
  2. Review and refine the generated queries:
    • Ensure 8-10 should-trigger queries cover different phrasings and edge cases
    • Ensure 8-10 should-not-trigger queries are near-misses (not obviously irrelevant)
    • Include casual speech, typos, and uncommon domain uses in should-trigger set
  3. Save the eval set to evals/evals.json in the skill directory

Good queries are realistic and specific (include file paths, context, domain details). Bad queries are overly generic ("format this data") or obviously irrelevant.

  1. Run python scripts/optimize_description.py <path> --eval-set evals/evals.json to score the current description and get improvement suggestions
  2. Recommend running /skill-forge eval <path> for full functional evaluation
Step 10: Generate Report
# Skill Review: [name]

## Health Score: [X]/100

## Summary
[2-3 sentence assessment]

## Scores by Category
| Category | Score | Notes |
|----------|-------|-------|
| Frontmatter | X/25 | [issues] |
| Triggering | X/20 | [issues] |
| Instructions | X/25 | [issues] |
| Structure | X/15 | [issues] |
| Scripts | X/10 | [issues] |
| Disclosure | X/5 | [issues] |

## Critical Issues (fix immediately)
- [issue 1]
- [issue 2]

## High Priority (fix within 1 week)
- [issue 1]

## Recommendations
- [suggestion 1]
- [suggestion 2]

## Suggested Test Queries
### Should Trigger
1. [query]
2. [query]
3. [query]

### Should NOT Trigger
1. [query]
2. [query]
3. [query]

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