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Review Loop

Community
sangrokjung
review-loop

Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified", "is this actually done", "maker checker", "second pair of eyes on this change". Not for prose, docs or typo edits.

Overview

Publishersangrokjung
Repositoryclaude-forge
Skill namereview-loop
Stars
853
Forks
183
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sangrokjung on GitHub. Read the source before you install it.

Installation

Install the Review Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sangrokjung/claude-forge/tree/main/skills/review-loop
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sangrokjung/claude-forge.git /tmp/claude-forge
mkdir -p .claude/skills
cp -r /tmp/claude-forge/skills/review-loop .claude/skills/review-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Loop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Loop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Loop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

review-loop — the independent adversarial loop

Nobody grades their own exam. The maker implements, a separate fresh checker tries to break the claim, and the two alternate until the checker approves the code as it stands right now.

When this applies

Any change to behaviour: executable code, shell/Python/JS/TS/SQL, CI/CD, hooks, commands, agent and skill definitions, runtime prompts, and configuration that changes what the system does.

Exempt: prose documentation, marketing copy, typos, and formatting that cannot change behaviour. When a documentation-only change also touches something a runtime reads, it is behavioural. If you are unsure which side a change falls on, treat it as behavioural.

File count and diff size are not exemptions. A one-line change to an authorization check is behavioural; a 900-line documentation reflow is not.

RiskLanes required
Ordinary behavioural change1 independent checker
Auth, authorization, payments, access-control rules, secrets, destructive data changes, irreversible deploys2 lanes, run by different checkers: correctness plus the risk-specific angle (runtime security, or rollback)

The loop

text
maker implements → targeted test / real-surface QA → fresh checker
     ↑                                                    │
     └────────── REQUEST_CHANGES ← findings ──────────────┘
                                                          │
                                          APPROVE on current code → done

There are exactly three verdicts: APPROVE, REQUEST_CHANGES, UNVERIFIED.

  • APPROVE is a completion candidate only when it was issued against the revision that is checked out now, with evidence produced from that same revision.
  • REQUEST_CHANGES means the maker fixes the findings surgically, re-runs the tests, and calls a fresh checker again. Fixing more than the findings restarts the argument.
  • UNVERIFIED means no judgement was reached: the checker never ran, returned empty, timed out, errored, was rate-limited, returned a malformed envelope, or reviewed a state that has since moved. It is not a pass. Retry with a different checker, runtime, or strategy.

If a checker modifies the source, it has joined the maker set and its verdict is void. A past maker can never be that change's checker.

Running it

  1. Implement. Keep the change inside the scope you declared. Unrelated cleanup that arrives in the same diff will be reviewed as part of it.

  2. Produce evidence before dispatch. Run the targeted tests and, when the change has a surface a person uses, exercise that surface the way they would. Record the exact commands and their exit status. Evidence from before your last edit is stale.

  3. Dispatch a fresh checker. Use the Task/Agent tool to spawn adversarial-reviewer in its own context, or a general-purpose agent given that agent's contract. Hand it:

    • the goal and acceptance criteria in one paragraph,
    • the scope (paths, or the diff range),
    • the commands you ran and what they returned,
    • the revision under review (git rev-parse HEAD).

    Do not hand it your conclusion. "I verified this works" is the claim under test, not context.

  4. Read the verdict, not the tone. A checker that praises the change but lists a HIGH finding has returned REQUEST_CHANGES. A checker that died mid-run has returned UNVERIFIED, however encouraging its last message was.

  5. Fix and re-dispatch. Every round gets a checker with a fresh context. Reusing the previous checker means asking someone to re-read their own conclusion.

  6. Stop when a current APPROVE exists for the code as it stands, backed by evidence from that same revision.

Re-verification etiquette

When you come back for round N+1, say so plainly:

  • state what changed since the last round, in one or two lines, and name the finding each edit addresses;
  • ask the checker to re-reproduce the original defect, not to take your word that it is gone;
  • explicitly invite it to attack the fix itself. Fixes introduce their own defects, and a fix written under review pressure is exactly where a second one hides.

Both of the defects worked through in docs/VERIFICATION-LOOP.md were found this way: the first review broke the guard, and the review of the fix broke the fix.

When rounds stop converging

Three rounds on the same strategy is not a reason to stop, and it is not a reason to keep going unchanged. Switch strategy: a different reproduction, a narrower test, a different checker or runtime, or a re-plan of the approach. See references/convergence-detection.md.

Escalate to the human when the same finding comes back REQUEST_CHANGES twice in a row: two failed attempts at one defect means the diagnosis is wrong, and a third attempt usually makes it worse. Report the finding, both attempted fixes, and the reproduction. See rules/adversarial-review.md.

Never report success when

  • the reviewed revision is not the revision that is checked out now;
  • there is no successful evidence from after the last edit;
  • the checker was the maker, or a fork of the maker's context;
  • a required lane is missing;
  • the checker modified code, returned an empty or malformed verdict, or timed out;
  • you are reusing an APPROVE from a different change or a different session.

In every one of those cases the honest report is what is missing, not "done".

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review Loop AI skill do?

Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified", "is this actually done", "maker checker", "second pair of eyes on this change". Not for prose, docs or typo edits.

Why use Review Loop on TypingMind?

Because you install it once and use it with any model. Review Loop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sangrokjung/claude-forge/tree/main/skills/review-loop. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review Loop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Loop?

As many as you like. As long as a model supports skills, you can use Review Loop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Loop AI skill free?

Yes. It is published on GitHub by sangrokjung under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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