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Agent skill

Review Plan

by penpot in penpot/penpot

Plan review flow — evaluate an implementation plan before it is executed, delegating the review to a subagent that follows the plan-review-criteria skill.

MPL-2.0Auto-check passedAgent Workflows

Install Review Plan

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

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

GitHub CLI
$ gh skill install penpot/penpot review-plan --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-plan .claude/skills/review-plan && 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-plan
GitHub stars
61k
Token cost
~841 tokens
SKILL.md length
487 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
MPL-2.0

At a glance

Plan review flow — evaluate an implementation plan before it is executed, delegating the review to a subagent that follows the plan-review-criteria skill.

  • Works in 4 steps: Determine the plan under review from the… → Delegate the review to the general… → When the subagent returns, output the… → …
  • The user asks to review a plan
  • 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 Plan is an agent skill from penpot/penpot. Plan review flow — evaluate an implementation plan before it is executed, delegating the review to a subagent that follows the plan-review-criteria skill. Use it when the user asks to review a plan, in any phrasing.

Its SKILL.md is about 840 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 Agent Workflows, covering Subagents and Planning. 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 a plan
  • In any phrasing

Example prompts

  • “/review-plan”

Workflow steps

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

  1. Determine the plan under review from the session context (for example, a plan just produced by /make-a-plan) or from a plan file path…
  2. Delegate the review to the general subagent (via the task tool), unless the user specifies another agent. Include in the prompt the…
  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 the next step based on the verdict. These are suggestions — the user decides, and any instruction…

What it can do on your machine

Read from SKILL.md and the folder at commit bcb7a83. 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 Plan loads about 841 tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 487 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~841

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 bcb7a83, republished under its MPL-2.0 licence (© penpot). 487 words, ~841 tokens.

Download SKILL.mdSave it as .claude/skills/review-plan/SKILL.md (or your agent's skills folder).
name
review-plan
description
Plan review flow — evaluate an implementation plan before it is executed, delegating the review to a subagent that follows the plan-review-criteria skill. Use it when the user asks to review a plan, in any phrasing.
slash
true

Review Plan

Act as a senior software engineer and perform a thorough review of an implementation plan.

When to use

  • The user asks to review a plan, in any phrasing: "review this plan", "does this plan look right?", "second opinion on the plan" — or runs /review-plan.
  • A plan was just produced (typically by /make-a-plan) and the user wants it evaluated before executing it.

Instructions

  1. Determine the plan under review from the session context (for example, a plan just produced by /make-a-plan) or from a plan file path given by the user (typically under .agents/plans/). If a file path is given, read the file first so the complete plan is in context.
  2. Delegate the review to the general subagent (via the task tool), unless the user specifies another agent. Include in the prompt the plan-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 the next step based on the verdict. These are suggestions — the user decides, and any instruction overrides them:
    • Approve → suggest /implement-plan to execute it.
    • Request changes → suggest /make-a-plan to make a plan to address the findings.
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 (196 more words)Show less

Instructions for the subagent

  1. Load the plan-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 documentation and testing memories before reviewing.
  3. Cada hallazgo debe tener un identificador único y estable, con el formato F1, F2, etc. El identificador debe aparecer en el título del hallazgo y no debe reutilizarse dentro de la misma revisión. Esto permite que el usuario pueda responder sobre un hallazgo concreto.
  4. 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. Judge the plan as the implementer would: every task executable without guessing, ordering follows the dependency graph, risks named.

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-plan of penpot/penpot.

Open the folder on GitHubat commit bcb7a83

Compare with similar skills

Review Plan 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 Plan compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Plan this skillpenpot/penpot61k—~841Automated safety check: PassMPL-2.0
Subagent Driven DevelopmentAsvarox/allkaraoke26137 repos~1.2kAutomated safety check: PassNone
Executing PlansGanyuanRan/Aegis1.3k1 repos~2.3kAutomated safety check: PassMIT
Execumputun/cc-thingz484—~8kAutomated safety check: PassMIT
Autopilot End-to-End Buildernick-vels/skills411—~2.5kAutomated safety check: NotesMIT
Workflow Orchestrationvxcozy/workflow-orchestration116—~1kAutomated safety check: PassMIT

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Categories

Questions about Review Plan

What does Review Plan do?

Plan review flow — evaluate an implementation plan before it is executed, delegating the review to a subagent that follows the plan-review-criteria skill. Review Plan is an agent skill from penpot/penpot. Plan review flow — evaluate an implementation plan before it is executed, delegating the review to a subagent that follows the plan-review-criteria skill.

When should I use Review Plan?

Review Plan fits situations like: the user asks to review a plan; in any phrasing.

How do I install Review Plan in Claude Code?

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

How do I install Review Plan in Codex?

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

Can I use Review Plan 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-plan -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-plan, .gemini/skills/review-plan, .github/skills/review-plan and .opencode/skills/review-plan in your project.

What does Review Plan need to run?

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

Does Review Plan 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 Plan 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 Plan use?

Review Plan 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 Plan use?

About 841 tokens (SKILL.md is roughly 3.4k 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 Plan?

Skills that share tags, products or a category with Review Plan: Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Executing Plans (GanyuanRan/Aegis, 1.3k stars), Exec (umputun/cc-thingz, 484 stars) and Autopilot End-to-End Builder (nick-vels/skills, 411 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Plan?

penpot (a GitHub organization) maintains it in penpot/penpot, which has 60,833 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 9, 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.