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Review Loop

Organization
Parcha-ai
review-loop

Drive every reviewer thread on a GitHub pull request to zero unresolved, iterating until the exit criterion is met. Covers bot reviewers such as Greptile and Devin and human reviewers. Fetches every inline comment, review, and issue comment, fixes what is actionable, replies, resolves bot threads through GraphQL, re-requests review once per push, and ends with a fixed-format report. Use when the user says "review loop", "address the review comments", "get this PR to zero unresolved", "greploop", or "make Greptile happy".

Overview

PublisherParcha-ai
Repositoryparcha-skills
Skill namereview-loop
Stars
65
Forks
5
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 Parcha-ai 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/Parcha-ai/parcha-skills/tree/main/review-loop/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/Parcha-ai/parcha-skills.git /tmp/parcha-skills
mkdir -p .claude/skills
cp -r /tmp/parcha-skills/review-loop/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

This skill has one job: take a GitHub PR with open review threads and drive every thread, from every reviewer, to zero unresolved, iterating until the target is met, then report what it did in a fixed format. It does not QA the running application; that is autoqa's job. It does not decide whether the diff is safe beyond what reviewers raised; that is blast-radius's job. It does not merge, and it does not mint credentials.

Inputs

InputRequiredDefault
Repository (owner/repo)noThe origin remote of the current checkout
PR numbernoThe PR for the current branch (gh pr view)
Target (open-for-review, review-clean, or merge-ready)noreview-clean
Trigger comment textno@greptile-apps review
--max-iterations NnoNone (no cap). Set only when the caller wants a hard stop
Gate commandnoNone. The caller or the repository supplies the command that runs tests and lint (for example the repo's make check); the skill only says where in the loop it runs
Base-branch syncnoNone. The caller or repository supplies the rebase or merge policy; the skill says where it runs

Auth precondition: the caller has authenticated gh before invoking this skill. The skill never mints, reads, or stores tokens. If gh auth status fails, stop and report it.

Iteration

Repeat until the target in step 9 is met. There is no iteration cap by default; a caller may pass --max-iterations to impose one. The loop still stops on timeout, a red gate it cannot fix, an auth failure, or a stall (see "Stop on stall"). Each iteration:

  1. Sync with the base branch if the caller supplied a sync policy. Run the gate command when the head changed or no exact-head result exists. Reuse valid exact-head evidence. A red gate stops the iteration; fix the gate before touching review comments.
  2. Fetch every open thread. See "Fetching everything". Do not filter to one reviewer.
  3. Triage by content, not by author. For each thread decide: actionable (a code change is needed), informational (no change, reply explains why), or false positive (reply explains why). Record the decision. A reply that proves inherited behavior or base parity without a code push keeps prior exact-head evidence valid. Do not rerun gates or restart proof and description work.
  4. Fix every actionable item in one pass. Read the file at the cited line, understand the comment in context, make the change.
  5. If code changed, run the gate command again. Do not push a red gate.
  6. If code changed, commit and push. Commit message names the iteration, for example Address review feedback (review-loop iteration 2).
  7. Reply and resolve. See "Human threads" and "Bot threads". A reply goes on the thread that raised the point, not in a new top-level comment.
  8. Re-request review once per push. A reply without a push does not require another request. See "Greptile specifics" and "Devin specifics". Wait for the results with a bounded poll.
  9. Check the target. For review-clean or merge-ready, require zero unresolved threads across every reviewer and, when Greptile is installed, a 5/5 score from its latest summary. For open-for-review, require a non-draft PR and focused proof. Automated review may remain pending.

If the caller set --max-iterations and the cap is reached with threads still open, stop and report them; do not start another iteration.

Fetching everything

Fetch all three comment sources on every iteration; a reviewer can use any of them.

bash
# Inline review threads with resolution state (GraphQL; paginate on endCursor)
gh api graphql -f query='...' # see references/graphql-queries.md

# Reviews (approve, request changes, comment) with bodies
gh api --paginate "repos/{owner}/{repo}/pulls/<PR_NUMBER>/reviews?per_page=100"

# Issue comments on the PR conversation tab (Greptile summaries, Devin notes, humans)
gh api --paginate "repos/{owner}/{repo}/issues/<PR_NUMBER>/comments?per_page=100"

A thread is open when isResolved is false. A review body or issue comment is a thread when it asks for a change or a reply; treat it as open until it is answered. Bot summaries that edit in place (Greptile does this) must be read by updated_at, not created_at.

Human threads

  • Reply on the thread with what was done and why, citing the commit. When nothing was changed, say what was considered and why the code stays as is.
  • Never resolve a thread a human opened without replying first. Prefer to leave resolution of human threads to the human unless the repository's policy says the author resolves.
  • Never dismiss a human review. A "changes requested" review is cleared by the reviewer re-reviewing, and re-requesting review is the only action the loop takes on it.

Bot threads

Resolve a bot thread after its item is fixed or answered. Use the GraphQL resolveReviewThread mutation, batched with aliases, and assert the result:

bash
gh api graphql -f query='
mutation {
  t1: resolveReviewThread(input: {threadId: "ID1"}) { thread { isResolved } }
  t2: resolveReviewThread(input: {threadId: "ID2"}) { thread { isResolved } }
}'

Every alias in the response must report isResolved: true. If one does not, re-fetch the thread and treat it as still open; do not count it as resolved. Batch up to 20 aliases per request. Full queries are in references/graphql-queries.md.

Resolving is only the GraphQL resolveReviewThread mutation. A REST reply (the pulls/{pr}/comments/{id}/replies endpoint) answers a thread but never resolves it, and a replied-but-unresolved thread still fails the zero-unresolved exit criterion. Always resolve through GraphQL, never assume a reply resolved anything.

Greptile specifics

  • Before posting a trigger comment, check for a run already in progress. Look at gh pr checks <PR_NUMBER> --json name,state and at repos/{owner}/{repo}/commits/<HEAD_SHA>/check-runs for a check whose name matches greptile (case-insensitive). If its state is PENDING or IN_PROGRESS, do not post; wait for it.
  • The trigger comment text is configurable; the default is @greptile-apps review. Post it at most once per push. Keep it to the mention: the reason for the re-review belongs in the thread replies, which Greptile reads, not in the trigger. A trigger must be a new comment; do not rely on an edited comment to start a review.
  • After the triggered review finishes (the summary's updated_at is later than the trigger), collapse the earlier trigger comments by the caller's agent identities with the GraphQL minimizeComment mutation, classifier OUTDATED. A trigger is any such comment that mentions @greptile-apps, including older prose triggers. Never collapse the latest trigger or a human's comment, and never delete a trigger. See references/graphql-queries.md.
  • Poll the check run at 10-second intervals for up to 10 minutes. A check run that has only just appeared but is still PENDING or IN_PROGRESS is not a result: wait for a terminal conclusion AND for the summary comment's updated_at to move past the trigger time before reading a score. A freshly-appeared check run beside an unchanged summary means Greptile has started but not finished, and reading the old summary then records a stale score.
  • On large PRs the tagged review may never create a check run for the new head; Greptile instead edits its existing summary comment. If no check run appears after a few attempts, poll the most recently updated Greptile issue comment and stop when its updated_at is later than the trigger comment and its body carries a score.
  • Read the score from three places and use the most recently updated one: the most recently updated Greptile-authored issue comment (it edits in place, so sort by updated_at), the PR body, and the latest review from greptile-apps[bot] or greptile-apps-staging[bot]. The score looks like 3/5, 5/5, or Confidence: 3/5.
  • Freshness gate: a score counts only for the current head. Resolve the commit it was produced for (the Greptile check run's head_sha, or a summary whose updated_at post-dates the head commit) and compare it to the PR's current head SHA. A score from an earlier commit is stale: re-trigger, wait, and never record it as the exit criterion.
  • Carry forward the items under "Prompt to fix all with AI" in the Greptile summary comment, even when the inline comment endpoint returns zero unresolved comments. They count as open until fixed or answered.
  • For review-clean or merge-ready, Greptile requires 5/5 and zero unresolved Greptile threads.

Devin specifics

  • Devin reviews when the PR becomes ready for review. A draft PR never gets a Devin review. If the PR is a draft and the caller wants Devin's review, mark it ready (gh pr ready) and say so in the report.
  • Devin posts inline threads and a summary issue comment. Triage them like any other thread; reply and resolve through the same GraphQL path.
  • Devin may report more issues than it posts ("N issues, M not posted by your settings"). The flags hidden by GitHub settings are not fetchable through the API and are not review threads: do not count them toward unresolved threads. Record them in the report as "M Devin flags not visible via API (see the Devin dashboard)" for a human, but the exit criterion is over the threads that actually exist on the PR.
  • Re-requesting Devin is a push; there is no trigger comment.

Stop on timeout

If a poll for review results times out, stop the loop and report the timeout. Never continue with stale or missing review results, and never count a thread as resolved because the reviewer did not answer.

Stop on stall

Stop and report when two consecutive iterations end with no code change and the only open items are ones already answered as informational or false positive. Re-triggering a reviewer that keeps repeating an answered point does not make progress. List those items under "Remaining" for a human.

Report

End with this table. Print it even when the loop stops early.

FieldValue
Repository / PRowner/repo#N
IterationsN (of max M, when a cap was set)
Threads found (per reviewer)Greptile N, Devin N, humans N
Resolved this runN
Replied, left open for a humanN
RemainingN, then one line each: path:line and a short quote
Final Greptile scoreX/5, or "not installed"
Stop reasonexit criterion met, stall, max iterations (when set), timeout, red gate, auth failure

The report goes to the caller. Do not post it as a new PR comment. A caller that wants it on the PR edits one comment marked <!-- agent-sticky:review-loop --> in place, using the upsert recipe in references/graphql-queries.md.

Write the report and every reply through unslop before posting.

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?

Drive every reviewer thread on a GitHub pull request to zero unresolved, iterating until the exit criterion is met. Covers bot reviewers such as Greptile and Devin and human reviewers. Fetches every inline comment, review, and issue comment, fixes what is actionable, replies, resolves bot threads through GraphQL, re-requests review once per push, and ends with a fixed-format report. Use when the user says "review loop", "address the review comments", "get this PR to zero unresolved", "greploop", or "make Greptile happy".

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/Parcha-ai/parcha-skills/tree/main/review-loop/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 Parcha-ai 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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