agent-ecosystem/skill-validator/review-skill

Review Skill Workflow

You are helping a skill author review an Agent Skill before publishing. This is a multi-step process: determine environment, verify prerequisites, run structural validation, review content, optionally run LLM scoring, and interpret results. Follow every step in order.

Step 0: Determine Environment

Check for saved configuration:

cat ~/.config/skill-validator/review-state.yaml 2>/dev/null

If the state file exists with prereqs_passed: true, offer:

Found saved settings — configured for [provider/structural-only] reviews.

1. Continue with saved settings — skip to Step 2

2. Re-run prerequisite checks

3. Change environment — switch provider or between LLM and structural-only

Option 1: read llm_scoring, provider, and cross_model from the file and skip to Step 2. Options 2-3: continue below.

If no state file exists, or the user chose to re-check/change, ask:

LLM scoring uses an Anthropic or OpenAI-compatible API, or the Claude CLI.

Without an API key or CLI, we run structural validation only.

1. Anthropic — use Claude via the Anthropic API (requires ANTHROPIC_API_KEY)

2. OpenAI — use GPT via the OpenAI API (requires OPENAI_API_KEY)

3. OpenAI-compatible — use a custom endpoint (Ollama, Groq, Azure, Together, etc.)

4. Claude CLI — use the locally authenticated claude binary (no API key needed)

5. Skip LLM scoring — structural validation only

Options 1-4: set LLM_SCORING=true and record the provider choice. Option 5: set LLM_SCORING=false. Run Step 1a only, then jump to Step 2.

If the user chose option 1 or 2, ask about cross-model comparison:

Scoring with a second model family gives more robust novelty scores, since

each model has different training data. This requires API keys for both

Anthropic and OpenAI.

1. Yes, compare across model families — score with both Anthropic and OpenAI

2. No, single provider is fine

Option 1: set CROSS_MODEL=true. Option 2: set CROSS_MODEL=false.

Do not offer cross-model comparison for option 3 (OpenAI-compatible) or option 4 (Claude CLI), since the second provider would need a standard Anthropic or OpenAI key.

After Step 1a, follow references/llm-scoring.md for API key checks before Step 2.

Step 1: Verify Prerequisites

1a. Check for skill-validator binary

skill-validator --version

If not found, search common locations (/usr/local/bin, /opt/homebrew/bin, ~/go/bin). If found but not on PATH, tell the user. If not found anywhere, follow references/install-skill-validator.md.

Do NOT proceed until this succeeds.

If LLM_SCORING=true, complete the provider checks in references/llm-scoring.md before continuing.

Save state after prerequisites pass

Persist state so future runs skip this step. Replace placeholders with actual values:

mkdir -p ~/.config/skill-validator
cat > ~/.config/skill-validator/review-state.yaml << 'EOF'
prereqs_passed: true
llm_scoring: <true or false>
provider: <anthropic, openai, openai-compatible, or claude-cli>
model: <model name if specified, or "default">
base_url: <custom base URL if openai-compatible, or omit>
cross_model: <true or false>
EOF

Step 2: Locate the Skill

Ask the user for the path to the skill they want to review, unless they have already provided it. Verify the path contains a SKILL.md file:

ls <path>/SKILL.md

If SKILL.md does not exist at the given path, tell the user this is not a valid skill directory and ask them to provide the correct path.

Step 3: Run Structural Validation

Run the full check suite:

skill-validator check <path>

Capture the exit code:

Exit 0: proceed. Exit 2: note warnings, proceed. Exit 1: list errors — these are blocking. The user must fix them before the skill can be published. Do NOT proceed to LLM scoring if exit code is 1.

Step 4: Content Review

Read the SKILL.md and any reference files, then evaluate each check below. Report which checks pass and which do not, with specific details on what is missing.

Flag any failing checks as areas the author should address. These are not blocking but should be resolved before publishing for best results.

Step 5: LLM Scoring and Interpretation

If LLM_SCORING=false, skip to Step 6.

If LLM_SCORING=true, follow the "Run LLM Scoring" and "Interpret LLM Scores" sections of references/llm-scoring.md.

Step 6: Present the Review Summary

If LLM_SCORING=true, follow the "Full Review Summary" section of references/llm-scoring.md. Include any failing content review checks from Step 4 in the action items.

If LLM_SCORING=false, present structural result, content review result, areas to address, and a self-assessment checklist using the scoring dimensions from assets/report.md. Note that LLM scoring was skipped; advise re-running with an API key or self-assessing against the report dimensions.

Example Review Summary Structure

Structure the final summary with these sections in order:

1. Structural validation — pass/fail with errors or warnings
2. SKILL.md scores — overall and per-dimension table
3. Reference scores — per-file table with overall and lowest dimension
4. Novelty assessment — mean novelty vs threshold of 3; list novel_info

per file for author verification

1. Action items — prioritized list of what to fix
2. Recommendation — ready to publish / minor revisions / significant rework