Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
PR Review Loop
Autonomous PR review loop with Greptile. Use when an agent creates a PR and needs to autonomously handle code review feedback — reading Greptile reviews, fixing issues, pushing fixes, re-triggering review, and auto-merging when score is 4/5+. Trigger on commands like "pr review {url}", "review my PR", or when a Greptile review webhook/poll delivers feedback.
skill openclawclawhub Free
Autonomous PR review loop with Greptile. Use when an agent creates a PR and needs to autonomously handle code review feedback — reading Greptile reviews, fixing issues, pushing fixes, re-triggering review, and auto-merging when score is 4/5+. Trigger on commands like "pr review {url}", "review my PR", or when a Greptile review webhook/poll delivers feedback.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/greptile-patterns.md, scripts/pr-review-loop.sh
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- cemoso
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
PR Review Loop
Autonomous cycle: Greptile reviews PR → agent fixes feedback → pushes → re-triggers → repeats until score ≥ 4/5 or max rounds.
Quick Start
When triggered with a PR URL or review payload: # Run the review loop bash scripts/pr-review-loop.sh <owner/repo> <pr-number> Or invoke steps manually — see below.
1. Fetch Review
# Get latest Greptile review gh api "/repos/{owner}/{repo}/pulls/{pr}/reviews" \ --jq '[.[] | select(.user.login == "greptile-apps[bot]")] | last' # Get inline comments gh api "/repos/{owner}/{repo}/pulls/{pr}/comments" \ --jq '[.[] | select(.user.login == "greptile-apps[bot]")]'
2. Parse Score
Look for confidence/quality score in review body. Greptile typically includes a score like Score: X/5 or Confidence: X/5. Extract it: Score ≥ 4/5 → auto-merge Score < 4/5 → fix issues No score found → treat as needing fixes if there are comments, otherwise merge
3. Auto-Merge (score ≥ 4)
gh pr merge <number> --merge --delete-branch --repo <owner/repo>
4. Fix Issues (score < 4)
- For each Greptile comment:
- Read the file and line referenced
- Understand the feedback
- Apply the fix
- Stage changes
- Commit with a descriptive message listing each fix:
- Address Greptile review feedback (round N)
- Fix X in path/to/file.ts
- Fix Y in path/to/other.ts
- Improve Z per reviewer suggestion
- Push and re-trigger:
- git push
- gh pr comment <number> --repo <owner/repo> --body "@greptileai review"
5. Track State
Maintain review-state.json in workspace: { "owner/repo#123": { "rounds": 2, "maxRounds": 5, "lastScore": 3, "sameScoreCount": 1 } } Update after each round. Check exit conditions: rounds ≥ 5 → merge anyway, notify Master sameScoreCount ≥ 2 (same score 2 rounds in a row) → merge anyway, notify Master
6. Escalation
Architectural decisions (review mentions architecture, design patterns, breaking changes) → ping Master on Telegram, don't auto-fix Max rounds reached → merge + notify Master with summary Unclear feedback → ask Master
Command Interface
Agents should respond to: pr review <url> — start review loop on a PR pr review <owner/repo#number> — same, by reference pr status — show active review loops and their state
References
See references/greptile-patterns.md for common Greptile feedback patterns and fix strategies.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts
- SKILL.md
- references/greptile-patterns.md
- scripts/pr-review-loop.sh