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Agent skill

Review Code

by penpot in penpot/penpot

Code review flow — review a diff, PR, or code change, delegating the review to a subagent that follows the code-review-criteria skill.

MPL-2.0Auto-check passedDevelopment

Install Review Code

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

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

GitHub CLI
$ gh skill install penpot/penpot review-code --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/penpot/penpot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/review-code .claude/skills/review-code && 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-code
GitHub stars
61k
Token cost
~796 tokens
SKILL.md length
449 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
MPL-2.0

At a glance

Code review flow — review a diff, PR, or code change, delegating the review to a subagent that follows the code-review-criteria skill.

  • Works in 4 steps: Determine what is being reviewed from… → Delegate the review to the general… → When the subagent returns, output the… → …
  • The user asks to review code
  • SKILL.md covers When to use, Instructions, Instructions for the subagent and User input, overrides and…
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Code is an agent skill from penpot/penpot. Code review flow — review a diff, PR, or code change, delegating the review to a subagent that follows the code-review-criteria skill. Use it when the user asks to review code or a PR, in any phrasing.

Its SKILL.md is about 800 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code review. The repository describes itself as: Penpot: The open-source design platform for Product teams that need scalable collaboration. The licence is MPL-2.0.

When your agent uses it

  • The user asks to review code
  • In any phrasing

Example prompts

  • “/review-code”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Determine what is being reviewed from the user context: a working-tree
  2. Delegate the review to the general subagent (via the task tool), unless the
  3. When the subagent returns, output the review to the user verbatim. Do not
  4. Right after the review, suggest how to proceed based on the findings. These

What it can do on your machine

Read from SKILL.md and the folder at commit cb1118f. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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.

Context cost

Review Code loads about 796 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 449 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~796

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 passed

The automated check found no risky patterns in SKILL.md.

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 penpot/penpot at commit cb1118f, republished under its MPL-2.0 licence (© penpot). 449 words, ~796 tokens.

Download SKILL.mdSave it as .claude/skills/review-code/SKILL.md (or your agent's skills folder).
name
review-code
description
Code review flow — review a diff, PR, or code change, delegating the review to a subagent that follows the code-review-criteria skill. Use it when the user asks to review code or a PR, in any phrasing.
slash
true

Review Code

Act as a senior software engineer and perform a thorough code review.

When to use

  • The user asks to review code, in any phrasing: "review this diff", "review the PR", "check my changes", "code review" — or runs /review-code.
  • A commit, branch, PR, or diff is ready and the user wants it assessed before merge.

Instructions

  1. Determine what is being reviewed from the user context: a working-tree diff, a commit range, a branch, a PR (number or URL), or specific files. If the target is ambiguous, ask before reviewing.
  2. Delegate the review to the general subagent (via the task tool), unless the user specifies another agent. Include in the prompt the code-review-criteria skill name and all user context.
  3. When the subagent returns, output the review to the user verbatim. Do not summarize it and do not act on its findings.
  4. Right after the review, suggest how to proceed based on the findings. These are suggestions — the user decides:
    • Approve (no required changes): say so — there is nothing to address.
    • Minor findings (nits): applying them directly as-is is fine once the review is done — no plan needed.
    • Substantive findings: suggest /make-a-plan to make a plan to address them.
Hard rule — read-only while reviewing

This flow is read-only for the duration of the review: from the moment it starts until the user considers the review finished (including any feedback, questions, or clarifications about it). During that period, never fix, implement, edit files or create commits — not even "obvious" fixes derived from the findings. Once the user explicitly states the review is done (or moves on to a different task), this rule no longer applies and you act as a normal build agent again.

Show full SKILL.md (163 more words)Show less

Instructions for the subagent

  1. Load the code-review-criteria skill and follow its process and output format.
  2. Read AGENTS.md (if present) and follow its instructions for finding and reading all related testing documentation from memories before reviewing.
  3. Return in your final message the COMPLETE review, verbatim, exactly as the skill instructs it to be produced. Do not summarize it — include the full structured review.
Strong rules for the subagent
  1. Do not invent problems. Every finding must be real and actionable.
  2. Read-only: do not modify any file and do not create a commit — reviewing never writes.
  3. Be specific and constructive. "This could be better" is not helpful — explain why and how.
  4. Prioritize by impact. One structural issue outweighs ten nits.
  5. Missing tests are an issue, not a suggestion. Report as a severity-tagged finding — never as a recommendation.
  6. Skip generated files, lockfile-only changes, and unrelated modifications unless they introduce security risks.

User input, overrides and additional context

$ARGUMENTS

© penpot, MPL-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

Just SKILL.md in .agents/skills/review-code of penpot/penpot.

Open the folder on GitHubat commit cb1118f

Compare with similar skills

Review Code 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 Code compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Code this skillpenpot/penpot61k—~796Automated safety check: PassMPL-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Backend Code Reviewlangflow-ai/langflow156k—~3.5kAutomated safety check: NotesMIT
Understand Diff AnalysisEgonex-AI/Understand-Anything86k1 repos~1.4kAutomated safety check: PassMIT
Mole Bug Patternstw93/Mole70k—~2kAutomated safety check: PassGPL-3.0

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    86k GitHub starsUsed in 1 repo~1.4k tokens
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  • Backend Code Review

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All 24 skills in this repo

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  • Create PR

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    PR flow — open a new PR for the current task branch (validates base branch, commits, issue and push state) or update an existing PR's title or description to match Penpot conventions.

    61k GitHub stars~1.4k tokensUpdated today
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  • Local CI

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    61k GitHub stars~822 tokensUpdated today
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  • Ste

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  • Code Review Criteria

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Categories

Questions about Review Code

What does Review Code do?

Code review flow — review a diff, PR, or code change, delegating the review to a subagent that follows the code-review-criteria skill. Review Code is an agent skill from penpot/penpot. Code review flow — review a diff, PR, or code change, delegating the review to a subagent that follows the code-review-criteria skill.

When should I use Review Code?

Review Code fits situations like: the user asks to review code; in any phrasing.

How do I install Review Code in Claude Code?

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

How do I install Review Code in Codex?

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

Can I use Review Code 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 penpot/penpot --skill review-code -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-code, .gemini/skills/review-code, .github/skills/review-code and .opencode/skills/review-code in your project.

What does Review Code need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Code is instructions for the agent only.

Does Review Code access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Code safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Code use?

Review Code is published under the MPL-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 Code use?

About 796 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Code?

Skills that share tags, products or a category with Review Code: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Backend Code Review (langflow-ai/langflow, 156k stars) and Understand Diff Analysis (Egonex-AI/Understand-Anything, 86k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Code?

penpot (a GitHub organization) maintains it in penpot/penpot, which has 60,798 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 7, 2026.

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