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ArcReel
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pr-ai-review-loop

Automates the pull request review-fix-push loop until all AI reviewers pass.

4.3k|859|Updated Feb 7, 2026
Search Tags:#CodeRabbit#Automation#ci#gitHub#CodeQL#Code review#Pull request
One-click install
npx skills add https://github.com/ArcReel/ArcReel --skill pr-ai-review-loop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pr-ai-review-loop
Source: https://github.com/ArcReel/ArcReel/tree/main/.agents/skills/pr-ai-review-loop
Command: npx skills add https://github.com/ArcReel/ArcReel --skill pr-ai-review-loop

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Driving a pull request through multiple AI reviewers (CodeRabbit, Gemini Code Assist, OpenAI Codex) means repeatedly polling for new comments, judging which are actionable, fixing code, pushing, and re-triggering reviews — a slow, error-prone manual cycle. This Skill runs that loop unattended until every reviewer passes or a convergence exit condition triggers.

Core Features & Use Cases

  • Unattended review convergence: Polls reviewer state via poll.sh, batches actionable comments, applies fixes following code-review discipline (YAGNI, deduplication), pushes once per batch, and waits with wait.sh between rounds.
  • Per-reviewer decision rules: Encodes trigger, reviewed-current-HEAD, actionable, and pass criteria for CodeRabbit, Gemini, Codex, and GitHub code scanning bots (CodeQL quality/security), including quota, rate-limit, and cold-start fallback handling.
  • Convergence guardrails and retrospective: Exits on round limits, diminishing returns, repeated-topic escalation, or full pass — producing a structured retrospective with ADR, CONTEXT, and follow-up issue candidates.
  • Use Case: After pushing a PR, ask the agent to drive it through AI review convergence; it will fix actionable findings from all three reviewers, handle CodeQL alerts, and report when every reviewer has passed.

Quick Start

Use the pr-ai-review-loop skill to drive this pull request through AI review convergence until all reviewers pass.

Frequently Asked Questions about pr-ai-review-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate AI code review fixes on a GitHub pull request?▼

Run the loop on a non-draft PR: it polls reviewer state with poll.sh, collects actionable comments from CodeRabbit, Gemini, and Codex, applies fixes in one batch, pushes, and repeats until all reviewers pass or a convergence exit condition is met.

Which AI reviewers does the pull request review loop support?▼

It supports CodeRabbit, Gemini Code Assist, and OpenAI Codex as participating reviewers, plus GitHub Code Quality and Advanced Security code scanning bots as exit-gate checks. Pure metrics bots like codecov are excluded from the loop.

Can the review loop handle CodeRabbit rate limits and quota errors?▼

Yes. It detects CodeRabbit rate-limit banners and quota alerts, waits for self-recovery, retries with a manual trigger once, and disables that reviewer for the PR if it stays blocked, recording the decision in the exit report.

When does the automated review loop stop running?▼

It exits when all reviewers pass and CodeQL gates are clean, or pauses for user decision after three rounds, two low-value push rounds, repeated reviewer topics across three HEADs, reviewer conflicts, or failures like unresponsive bots and gh authentication errors.

What tools does the review loop require to run?▼

It requires the GitHub CLI (gh) authenticated with repo scope, jq for JSON processing, and bash. The scripts poll.sh, query.sh, wait.sh, and classify_commits.sh orchestrate all GitHub API interactions.

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