Review Skill
MongoDBOfficialCreated by mongodb
- Skill
- Review and audit
- Agent tooling
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured process for SMEs to review Agent Skills before publication. It combines automated structural validation via a CLI tool with LLM-based content scoring to identify areas for improvement. The workflow guides users through prerequisite checks, structural validation, content evaluation, and the generation of a comprehensive review summary.
What it does
- Performs structural validation of Agent Skills to detect errors and warnings.
- Evaluates skill content quality using LLM-based scoring.
- Assesses content novelty to determine the value added by the skill.
- Provides a standardized summary report including action items and recommendations.
- Guides users through prerequisite installation and environment configuration.
- Offers a self-assessment framework for reviewing skill dimensions.
When to use it
- When preparing an Agent Skill for publication.
- When you need to verify the structural integrity of a skill directory.
- When you want to assess the content quality and novelty of a skill.
- When you need a structured report to guide revisions for an Agent Skill.
Advantages
- Automates the detection of structural issues that block publication.
- Provides objective scoring metrics for content quality and novelty.
- Helps identify redundant content that can be compressed or removed.
- Standardizes the review process across different skill authors.
- Offers actionable feedback based on specific scoring dimensions.
Usage examples
- “Review my agent skill located at ./my-new-skill”
- The agent will initiate the review workflow, check for prerequisites, run structural validation, and provide a summary report.
- “I want to perform a full quality review of my skill including LLM scoring.”
- The agent will configure the environment for LLM scoring, verify the Claude CLI, and generate a detailed report with novelty and quality scores.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/mongodb/review-skill/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip review-skill.zip -d .agents/skills
Documentation by mongodb
Review Skill Workflow
You are helping an SME 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/nullIf the state file exists with prereqs_passed: true, offer:
Found saved settings — configured for [full/structural-only] reviews.
Continue with saved settings — skip to Step 2
Re-run prerequisite checks
Change environment — switch between full and structural-only
Option 1: read llm_scoring 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 evaluates content quality across multiple dimensions.
Yes, run LLM scoring — full review with LLM scoring
No, skip LLM scoring — structural validation only
Option 1: set LLM_SCORING=true.
Option 2: set LLM_SCORING=false. Run Step 1a only, then jump to Step 2.
Step 1: Verify Prerequisites
1a. Check for skill-validator binary
skill-validator --versionIf 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.
If --version is not at least v1.5.1, help the user upgrade with
brew upgrade skill-validator or
go install github.com/agent-ecosystem/skill-validator/cmd/skill-validator@latest.
Do NOT proceed until this succeeds.
1b. Check for claude CLI (LLM scoring only)
If LLM_SCORING=true, verify the Claude CLI is available:
claude --versionIf not found, tell the user to install Claude Code:
macOS:
curl -fsSL https://claude.ai/install.sh | bashOther platforms: follow the Claude Code quickstart guide
The user must authenticate by running claude interactively before continuing.
Do NOT proceed with LLM scoring until this succeeds.
Save state after prerequisites pass
Persist state so future runs skip this step. Replace <true or false> with
the actual LLM_SCORING value:
mkdir -p ~/.config/skill-validator
cat > ~/.config/skill-validator/review-state.yaml << 'EOF'
prereqs_passed: true
llm_scoring: <true or false>
EOFStep 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.mdIf 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 code | Meaning |
|---|---|
| 0 | Clean — no errors or warnings |
| 1 | Errors found — must fix before publishing |
| 2 | Warnings only — review but not blocking |
| 3 | CLI/usage error — check the command |
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.
| Check | Criteria |
|---|---|
| Examples | Does the skill provide examples of expected inputs and outputs? |
| Edge cases | Does the skill document common edge cases or failure modes? |
| Scope-gating | Does the skill define when to stop/continue, prerequisites, and conditions for branching paths? |
| MongoDB data access | If the skill needs MongoDB contextual data, does it instruct agents to use the MCP server for auth and tool calls? Skip if not applicable. |
Flag any failing checks as areas the SME 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 LLM scoring enabled or self-assessing against the report
dimensions.
Example Review Summary Structure
Structure the final summary with these sections in order:
Structural validation — pass/fail with errors or warnings
SKILL.md scores — overall and per-dimension table
Reference scores — per-file table with overall and lowest dimension
Novelty assessment — mean novelty vs threshold of 3; list
novel_infoper file for SME verificationAction items — prioritized list of what to fix
Recommendation — ready to publish / minor revisions / significant rework
Files
- ATTRIBUTION.mdAdded by AutoGineer929 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.1 KiB
- assets/report.md6.8 KiB
- references/install-skill-validator.md1.1 KiB
- references/llm-scoring.md4.8 KiB
references/llm-scoring.md
LLM Scoring Flow
LLM scoring steps using the claude-cli provider. This provider shells out to
the locally installed claude binary — no API keys are needed. It uses the
CLI's existing authentication (e.g., company or team subscription).
Only follow this if the user selected LLM scoring in Step 0.
Accuracy caveat
The Claude CLI loads local context (CLAUDE.md files, project memory, rules) into each scoring call. This extra context may influence scores, making them less reproducible across environments compared to API-based providers.
Run LLM Scoring (after structural validation passes)
Check for cached scores:
skill-validator score report <path> -o json 2>/dev/nullIf scored output exists, use --rescore to generate fresh scores (content may
have changed since the last run):
skill-validator score evaluate <path> --provider claude-cli --full-content --display files -o json --rescoreIf no cached scores exist, run without --rescore:
skill-validator score evaluate <path> --provider claude-cli --full-content --display files -o jsonAfter scoring completes, run the comparison report:
skill-validator score report <path> -o jsonCapture both outputs for interpretation.
Error handling
| Error | Cause | Fix |
|---|---|---|
claude: command not found | CLI not installed | macOS: curl -fsSL https://claude.ai/install.sh | bash; other platforms: quickstart guide |
claude auth error | CLI not authenticated | Run claude interactively to complete login |
| Rate limit / 429 | Too many concurrent calls | Wait and retry; scoring is sequential by default |
Interpret LLM Scores
Read ../assets/report.md for the full interpretation framework, then present results to the user following that structure.
Quality thresholds
There are no hard pass/fail gates on most dimensions. Use these guidelines:
Overall >= 3.5: The skill is in good shape across most dimensions.
Overall 2.5-3.5: The skill needs work in specific areas. Identify which dimensions are dragging the score down and advise accordingly.
Overall < 2.5: The skill needs significant revision across multiple areas.
Any dimension at 2 or below: Flag this specifically as an area needing attention. Explain what a low score on that dimension means and suggest concrete improvements.
Novelty is the key differentiator
If mean novelty across all files is below 3, the skill may not justify its context window cost. A low-novelty skill restates what models already know. The critical question: does this skill teach the agent something it genuinely doesn't know?
Surface novel_info for SME review
Each scored file includes a novel_info field describing what the LLM judge
identified as genuinely novel content. Present these details to the SME for
each file, because:
Verification: The LLM may misidentify something as novel or miss truly novel content. The SME should confirm the claims are accurate.
Focus: Items listed in
novel_inforepresent the skill's highest-value content. If these details are wrong or missing, the novelty score is unreliable.Trimming guidance: Content NOT mentioned in
novel_infois likely restating common knowledge and is a candidate for compression or removal.
If novelty is low, advise the SME to:
Identify which sections contain information that is NOT available in public documentation or model training data (proprietary APIs, internal conventions, non-obvious gotchas, organization-specific workflows).
Cut or compress sections that merely restate common knowledge.
Focus the skill on the genuinely novel content.
For more context on why novelty matters and how to think about skill quality, refer the SME to: https://agentskillreport.com/
Important scoring caveat
Scores are generated using Anthropic Claude, so novelty reflects what Claude knows from training data. Other model families may produce different novelty scores due to different training data coverage.
Full Review Summary
When LLM scoring was performed, present the review summary with:
Structural validation result: Pass/fail, with any errors or warnings.
LLM score summary: Overall score and per-dimension breakdown for SKILL.md and references (if any).
Areas to address: Specific dimensions that need improvement, with concrete suggestions.
Novelty assessment: Whether the skill provides sufficient novel value, with specific guidance if it doesn't. Include the
novel_infodetails for each file so the SME can verify accuracy and identify what to keep or cut.Recommendation: Whether the skill is ready to publish, needs minor revisions, or needs significant rework.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs network access
The author says: Requires skill-validator CLI and claude CLI for LLM scoring. LLM scoring can be skipped for structural-only review.
Security
What our scanner noticed· Medium risk
- Instructions that use the network
- Downloads piped into a shell
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- mongodb
- Publisher
- MongoDB (Official)
- Source
- mongodb/agent-skills · tools/review-skill · commit 155bb9c
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by mongodb or MongoDB.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download review-skill.zip