Agent skill

Review Loop

by OpenCyphal in OpenCyphal/pycyphal

Multi-agent review/refine loop. An agent skill from OpenCyphal/pycyphal.

MITAuto-check passedTesting & QA

Install Review Loop

skills CLI
$ npx skills add OpenCyphal/pycyphal --skill review-loop -a claude-code

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

GitHub CLI
$ gh skill install OpenCyphal/pycyphal review-loop --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/OpenCyphal/pycyphal.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/review-loop .claude/skills/review-loop && 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-loop
GitHub stars
142
Token cost
~817 tokens
SKILL.md length
444 words
Files
1
Skills in repo
3
Repo updated
First seen
Licence
MIT

At a glance

Multi-agent review/refine loop. An agent skill from OpenCyphal/pycyphal.

  • Tasks that involve Project management
  • SKILL.md covers The reviewer pair, Reviewers are read-only, Reviewers do not re-run the… and Consolidate and act, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Loop is an agent skill from OpenCyphal/pycyphal. Multi-agent review/refine loop. Use after a change or milestone, or when asked to review work: dispatch a fresh-context full-spectrum reviewer plus a dissimilar correctness reviewer, consolidate and fix, add a regression test for every defect, and repeat until a round is clean.

Its SKILL.md is about 820 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 Testing & QA, covering Project management. It works with Python and Linux. The repository describes itself as: Python implementation of the Cyphal protocol stack. The licence is MIT.

When your agent uses it

  • Tasks that involve Project management

Example prompts

  • “/review-loop”

What it can do on your machine

Read from SKILL.md and the folder at commit 34530f6. 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 Loop loads about 817 tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 444 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~817

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 OpenCyphal/pycyphal at commit 34530f6, republished under its MIT licence (© OpenCyphal). 444 words, ~817 tokens.

Download SKILL.mdSave it as .claude/skills/review-loop/SKILL.md (or your agent's skills folder).
name
review-loop
description
Multi-agent review/refine loop. Use after a change or milestone, or when asked to review work: dispatch a fresh-context full-spectrum reviewer plus a dissimilar correctness reviewer, consolidate and fix, add a regression test for every defect, and repeat until a round is clean.

Adversarial review/refine loop

After a change or milestone, or when prompted, dispatch fresh-context review agents at MAXIMUM THINKING EFFORT, then consolidate, fix, and repeat. The goal is adversarial, diverse, independent coverage.

The prompts given to the agents shall be extremely terse, at most a few sentences. Giving excessive detail may constrain their thinking causing the tunnel vision syndrome. They must be given the opportunity to look at the work without bias or prejudice.

The reviewer pair

Run two reviewers in parallel per round:

  • An ultrathink Claude agent with the FULL-SPECTRUM remit, in priority order: functional CORRECTNESS and ROBUSTNESS first, then SIMPLIFICATION opportunities, ARCHITECTURAL CLEANLINESS and CODE QUALITY, and POLICY/STYLE compliance with the project's own docs.

  • Codex running the most advanced model in ultra effort focusing on CORRECTNESS only, to maximize perspective diversity and minimize blind spots.

Reviewers are read-only

Review agents must not modify the worktree or run mutating commands. If one needs a mutable environment, it copies the worktree elsewhere.

Reviewers do not re-run the project test suites

The tests normally should already be green when the review loop is invoked; re-running them duplicates work and, for the broad sessions, wastes minutes of compute per round. State this in the reviewer prompts. Reviewer effort goes instead into adversarial counterexamples for behaviors the existing tests do NOT cover, executed in a scratch clone. Probes must run under the repo's own test interpreter (e.g. .nox/tests/bin/python, which mutates nothing) rather than whatever is on PATH: a version-skewed interpreter or dependency set can produce findings that do not apply to the project or miss ones that do. Reproducing their own findings before reporting remains mandatory.

Show full SKILL.md (172 more words)Show less

Consolidate and act

When all reviewers return, merge their findings, discard the noise, and fix what is real. For every correctness defect, add a regression test verified to fail before the fix and pass after.

When to stop

A round is clean when the reviewers surface only trivial feedback or none; the first clean round ends the loop. Do not chase literal zero feedback: with no real issues left, agents degrade into nitpicking, so a round is clean as soon as significant findings cease.

Operational notes

High-effort agents can go silent for a long time — set generous timeouts and do not assume a quiet agent is stuck. Have agents background long-running commands (tests especially); blocking on a foreground command is a common cause of stream-idle timeouts.

Some headless agents hang waiting on stdin (like Codex) — redirect from /dev/null.

Retry agents that fail on a transient or connection error until they succeed. If an agent gets stuck or hits a security guardrail, try resuming it first instead of restarting its work from scratch.

© OpenCyphal, MIT. 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 .claude/skills/review-loop of OpenCyphal/pycyphal.

Open the folder on GitHubat commit 34530f6

Compare with similar skills

Review Loop 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 Loop compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Loop this skillOpenCyphal/pycyphal142—~817Automated safety check: PassMIT
Apple Container Test RunnerRustPython/RustPython22k—~467Automated safety check: PassMIT
Cross Platform Pathsmicrosoft/vscode-python-environments141—~1.5kAutomated safety check: PassMIT
Xhs Auto PublisherDjangoPeng/agentic-ai152—~570Automated safety check: PassMIT
CCC Dashboard Runtime Testingamirfish1/claude-command-center177—~1.1kAutomated safety check: PassCustom licence
Verify Changes389ds/389-ds-base294—~1.9kAutomated safety check: PassCustom licence

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Works with

Questions about Review Loop

What does Review Loop do?

Multi-agent review/refine loop. An agent skill from OpenCyphal/pycyphal. Review Loop is an agent skill from OpenCyphal/pycyphal. Multi-agent review/refine loop.

When should I use Review Loop?

Review Loop fits situations like: tasks that involve Project management.

How do I install Review Loop in Claude Code?

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

How do I install Review Loop in Codex?

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

Can I use Review Loop 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 OpenCyphal/pycyphal --skill review-loop -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-loop, .gemini/skills/review-loop, .github/skills/review-loop and .opencode/skills/review-loop in your project.

What does Review Loop need to run?

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

Does Review Loop 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 Loop 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 Loop use?

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

How many tokens does Review Loop use?

About 817 tokens (SKILL.md is roughly 3.3k 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 Loop?

Skills that share tags, products or a category with Review Loop: Apple Container Test Runner (RustPython/RustPython, 22k stars), Cross Platform Paths (microsoft/vscode-python-environments, 141 stars), Xhs Auto Publisher (DjangoPeng/agentic-ai, 152 stars) and CCC Dashboard Runtime Testing (amirfish1/claude-command-center, 177 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Loop?

OpenCyphal (a GitHub organization) maintains it in OpenCyphal/pycyphal, which has 142 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on July 24, 2026.

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