New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Official agent skill

Review Skill

by mongodb in mongodb/agent-skills

Review a proposed Agent Skill for structural validity and content quality before publishing.

OfficialApache-2.0Auto-check: notesAI & LLM Engineering

Install Review Skill

skills CLI
$ npx skills add mongodb/agent-skills --skill review-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mongodb/agent-skills review-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mongodb/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tools/review-skill .claude/skills/review-skill && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-skill
GitHub stars
190
Token cost
~1.5k tokens
SKILL.md length
719 words
Files
4 (incl. references, assets)
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

Review a proposed Agent Skill for structural validity and content quality before publishing.

  • Works in 7 steps: Determine Environment → Verify Prerequisites → Locate the Skill → …
  • A user wants to review
  • SKILL.md covers Step 0: Determine Environment, Step 1: Verify Prerequisites, Step 2: Locate the Skill and Step 3: Run Structural…, plus 4 more sections
  • Calls claude, brew and go; reaches claude.ai

What it does

Review Skill is an agent skill from mongodb/agent-skills, published by the product's own GitHub organization. Review a proposed Agent Skill for structural validity and content quality before publishing. Runs the skill-validator CLI to check for structural issues, scores the skill with an LLM judge, and interprets results to advise SMEs on what to address. Use when a user wants to review, validate, or quality-check an Agent Skill.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files and assets (for example `assets/report.md`, `references/install-skill-validator.md` and `references/llm-scoring.md`). Compatibility notes: Requires skill-validator CLI and claude CLI for LLM scoring. LLM scoring can be skipped for structural-only review.

It sits in AI & LLM Engineering, covering LLM evaluation. The repository describes itself as: Use the official MongoDB Skills with your favorite coding agent to build faster. The licence is Apache-2.0.

When your agent uses it

  • A user wants to review
  • Quality-check an Agent Skill

Example prompts

  • “/review-skill”

Requirements

  • Compatibility (from SKILL.md): Requires skill-validator CLI and claude CLI for LLM scoring. LLM scoring can be skipped for structural-only review.

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Determine Environment
  2. Verify Prerequisites
  3. Locate the Skill
  4. Run Structural Validation
  5. Content Review
  6. LLM Scoring and Interpretation
  7. Present the Review Summary

What it can do on your machine

Read from SKILL.md and the folder at commit 18b014e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • claude
    • brew
    • go
    • curl
    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • claude.ai

    Also links to:

    • code.claude.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires skill-validator CLI and claude CLI for LLM scoring. LLM scoring can be skipped for structural-only review.

    From compatibility in the SKILL.md frontmatter.

Context cost

Review Skill loads about 1.5k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 719 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePipes a well-known installer script into a shellSKILL.md:79
    - **macOS**: `curl -fsSL https://claude.ai/install.sh | bash`

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mongodb/agent-skills at commit 18b014e, republished under its Apache-2.0 licence (© mongodb). 719 words, ~1,547 tokens.

Download SKILL.mdSave it as .claude/skills/review-skill/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
review-skill
description
Review a proposed Agent Skill for structural validity and content quality before publishing. Runs the skill-validator CLI to check for structural issues, scores the skill with an LLM judge, and interprets results to advise SMEs on what to address. Use when a user wants to review, validate, or quality-check an Agent Skill.
compatibility
Requires skill-validator CLI and claude CLI for LLM scoring. LLM scoring can be skipped for structural-only review.
metadata.author
mongodb
metadata.version
1.0

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:

bash
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 [full/structural-only] reviews.

  1. Continue with saved settings — skip to Step 2
  2. Re-run prerequisite checks
  3. 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.

  1. Yes, run LLM scoring — full review with LLM scoring
  2. 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
bash
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.

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:

bash
claude --version

If not found, tell the user to install Claude Code:

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:

bash
mkdir -p ~/.config/skill-validator
cat > ~/.config/skill-validator/review-state.yaml << 'EOF'
prereqs_passed: true
llm_scoring: <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:

bash
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:

bash
skill-validator check <path>

Capture the exit code:

Exit codeMeaning
0Clean — no errors or warnings
1Errors found — must fix before publishing
2Warnings only — review but not blocking
3CLI/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.

Show full SKILL.md (287 more words)Show less

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.

CheckCriteria
ExamplesDoes the skill provide examples of expected inputs and outputs?
Edge casesDoes the skill document common edge cases or failure modes?
Scope-gatingDoes the skill define when to stop/continue, prerequisites, and conditions for branching paths?
MongoDB data accessIf 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:

  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 SME verification
  5. Action items — prioritized list of what to fix
  6. Recommendation — ready to publish / minor revisions / significant rework

© mongodb, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references, assets) in tools/review-skill of mongodb/agent-skills.

  • SKILL.md
  • assets/report.md
  • references/install-skill-validator.md
  • references/llm-scoring.md

Open the folder on GitHubat commit 18b014e

Compare with similar skills

Review Skill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Skill compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Skill this skillmongodb/agent-skills190—~1.5kAutomated safety check: NotesApache-2.0
LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs13k8 repos~3kAutomated safety check: PassMIT
Azure AI Projects Python SDKmicrosoft/skills3.1k6 repos~2.8kAutomated safety check: PassMIT
Fine-Tuning ExpertJeffallan/claude-skills12k1 repos~1.7kAutomated safety check: PassMIT
Looperksimback/looper710—~2.7kAutomated safety check: NotesMIT
Hugging Face Local Model Evalshuggingface/skills11k2 repos~1.6kAutomated safety check: PassApache-2.0

Similar skills

  • LLM Benchmarking with lm-evaluation-harness

    Orchestra-Research/AI-Research-SKILLs

    Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.

    13k GitHub starsUsed in 8 repos~3k tokens
    AI & LLM EngineeringAuto-check passed

  • Azure AI Projects Python SDK

    microsoft/skills

    Official

    Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.

    3.1k GitHub starsUsed in 6 repos~2.8k tokens
    AI & LLM EngineeringAuto-check passed

  • Fine-Tuning Expert

    Jeffallan/claude-skills

    Guides LLM fine-tuning with LoRA and QLoRA through Hugging Face PEFT, from dataset validation and training checks to adapter merging, quantization and deployment.

    12k GitHub starsUsed in 1 repo~1.7k tokens
    AI & LLM EngineeringAuto-check passed

  • Looper

    ksimback/looper

    Scaffold a well-designed agent loop with best-practice coaching and a cross-model review council.

    710 GitHub stars~2.7k tokensUpdated 2 mo ago
    AI & LLM EngineeringAuto-check: notes

  • Hugging Face Local Model Evals

    huggingface/skills

    Official

    Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.

    11k GitHub starsUsed in 2 repos~1.6k tokens
    AI & LLM EngineeringAuto-check passed

  • Agent Eval Engineering

    langchain-ai/langchain-skills

    Official

    Builds agent evaluations in stages: inspect the repository and traces, agree a Task Spec with you, then build, audit and run a Harbor task with an independent verifier.

    1.3k GitHub stars~4k tokensUpdated 3 days ago
    AI & LLM EngineeringAuto-check passed

More from mongodb/agent-skills

All 9 skills in this repo

  • Mongodb Query Optimizer

    mongodb/agent-skills

    Official

    Help with MongoDB query optimization and indexing. An agent skill from mongodb/agent-skills.

    190 GitHub starsUsed in 2 repos~2.6k tokens
    Auto-check passed

  • Mongodb MCP Setup

    mongodb/agent-skills

    Official

    Guide users through configuring key MongoDB MCP server options.

    190 GitHub starsUsed in 1 repo~3.3k tokens
    Auto-check passed

  • Mongodb Schema Design

    mongodb/agent-skills

    Official

    MongoDB schema design patterns and anti-patterns. An agent skill from mongodb/agent-skills.

    190 GitHub starsUsed in 2 repos~3.4k tokens
    Auto-check passed

  • Mongodb Natural Language Querying

    mongodb/agent-skills

    Official

    Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents.

    190 GitHub starsUsed in 1 repo~2.4k tokens
    Auto-check passed

  • Mongodb Connection

    mongodb/agent-skills

    Official

    Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language.

    190 GitHub starsUsed in 1 repo~3.5k tokens
    Auto-check passed

  • Mongodb Search And AI

    mongodb/agent-skills

    Official

    Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions.

    190 GitHub starsUsed in 1 repo~1.7k tokens
    Auto-check passed

Categories

Questions about Review Skill

What does Review Skill do?

Review a proposed Agent Skill for structural validity and content quality before publishing. Review Skill is an agent skill from mongodb/agent-skills, published by the product's own GitHub organization. Review a proposed Agent Skill for structural validity and content quality before publishing.

When should I use Review Skill?

Review Skill fits situations like: A user wants to review; quality-check an Agent Skill.

How do I install Review Skill in Claude Code?

Run `npx skills add mongodb/agent-skills --skill review-skill -a claude-code`. Or copy the skill folder (tools/review-skill in mongodb/agent-skills) into .claude/skills/review-skill in your project. Claude Code loads it when a task matches its description.

How do I install Review Skill in Codex?

Run `npx skills add mongodb/agent-skills --skill review-skill -a codex`. Or copy the skill folder (tools/review-skill in mongodb/agent-skills) into .agents/skills/review-skill in your project. Codex loads it when a task matches its description.

Can I use Review Skill in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mongodb/agent-skills --skill review-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-skill, .gemini/skills/review-skill, .github/skills/review-skill and .opencode/skills/review-skill in your project.

What does Review Skill need to run?

Going by SKILL.md and its folder, Review Skill needs the command-line tools its instructions call (claude, brew, go, curl and bash). Compatibility (from SKILL.md): Requires skill-validator CLI and claude CLI for LLM scoring. LLM scoring can be skipped for structural-only review..

Does Review Skill access the network?

SKILL.md names 2 domains. In commands or code: claude.ai; the agent is likely to contact it when it follows the instructions. As links in the text: code.claude.com. This is read from the text; nothing was executed.

Is Review Skill safe to install?

Our automated static check of SKILL.md found notes only (pipes a well-known installer script into a shell), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Review Skill use?

Review Skill is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Skill use?

About 1.5k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Review Skill?

Skills that share tags, products or a category with Review Skill: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Azure AI Projects Python SDK (microsoft/skills, 3.1k stars), Fine-Tuning Expert (Jeffallan/claude-skills, 12k stars) and Looper (ksimback/looper, 710 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Skill?

mongodb (a GitHub organization, an official publisher) maintains it in mongodb/agent-skills, which has 190 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 7, 2026.

Source: mongodb/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.