review-loop

review-loop is a skill for Claude Code from Parcha-ai/parcha-skills. It costs 118 tokens per session (2,847 once invoked), scanned A, original, MIT.

A workflow for clearing all unresolved review conversations on a GitHub pull request, including comments from automated and human reviewers, through bounded rounds of fixes and replies.

In plain words
What is it for?
Use it to fetch review feedback, fix actionable issues, reply to reviewers, and resolve automated-review threads until the pull request is ready for its chosen review state.
Why use it?
Pull requests can have review comments scattered across inline threads, reviews, and general comments. This keeps those requests from being forgotten while leaving application testing and merging to other steps.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the review-loop plugin — 1 skill shipped together

Good fit Use it to fetch review feedback, fix actionable issues, reply to reviewers, and resolve automated-review threads until the pull request is ready for its chosen review state.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/parcha-ai/parcha-skills/review-loop
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Parcha-ai/parcha-skills --skill review-loop
Clone the repo
git clone --depth 1 https://github.com/Parcha-ai/parcha-skills

Made for: Claude Code.

Or install review-loop, the plugin that ships this one along with the rest of its 1 skill.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for review-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/parcha-ai/parcha-skills/review-loop/github.svg)](https://agentmods.dev/skills/parcha-ai/parcha-skills/review-loop)
Your own site
<a href="https://agentmods.dev/skills/parcha-ai/parcha-skills/review-loop"><img src="https://agentmods.dev/badge/skills/parcha-ai/parcha-skills/review-loop/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for review-loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/parcha-ai/parcha-skills/review-loop"><img src="https://agentmods.dev/badge/skills/parcha-ai/parcha-skills/review-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,847 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.02847
Opus 5.5 $0.00047 $0.01139
Sonnet 5.5 $0.00024 $0.00569
Haiku 4.5 $0.00012 $0.00285

Measured 1mo ago against content hash 313bb9fbc5dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

review-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

review-loop/skills/review-loop/SKILL.md · 203 lines

How it starts

The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Input Required Default
Repository (owner/repo) no The origin remote of the current checkout
PR number no The PR for the current branch (gh pr view)
Target (open-for-review, review-clean, or merge-ready) no review-clean
Trigger comment text no @greptile-apps review
--max-iterations N no None (no cap). Set only when the caller wants a hard stop
Gate command no None. 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 sync no None. 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.

Read the full file on GitHub · 203 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +8 lines · +6 tokens per session 313bb9fbc5dc
  2. 1mo ago Changed · +12 lines a99b365c9d3f
  3. 1mo ago First seen · 183 lines · 112 tokens per session scan A cacabae7aeaf

Subscribe to this mod's changes

review-loop is a skill published in the GitHub repository Parcha-ai/parcha-skills (126 stars, last pushed 15d ago), licensed MIT. It adds 118 tokens to every session and 2,847 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-17.

Related

review-loop compared with skills from other repositories

review-loop next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
review-loopParcha-ai/parcha-skillsSkill12615d agoMITA118Claude Code
autoreviewopenclaw/clawhubSkill9.5ktodayMITA17Claude Code, Codex
agent-teams-simplify-and-hardenLeoYeAI/openclaw-master-skillsSkill2.2k6d agoMITA115Claude Code, Codex
omh-code-reviewrlaope/oh-my-hermesSkill5.2ktodayMITA48Claude Code, Codex
revdiff-planumputun/revdiffSkill9472d agoMITA84Claude Code
explore-unknownsdzhng/skillsSkill1.4ktodayMITA162Claude Code, Codex
re0-mergeLilMGenius/paperthinSkill1.4k4d agoMITA70Claude Code