open claw GitHub bot

open claw GitHub bot.md

Yes, absolutely — this is a well-trodden path and it works cleanly. Here's exactly how the chain connects.

The Pipeline: GitHub Action → Self-Hosted Runner → OpenClaw

The flow is straightforward: scaleway

PR opened on GitHub
  → triggers GitHub Actions workflow
    → job runs on your self-hosted Mac Mini M4 runner
      → runner executes OpenClaw with gh CLI
        → OpenClaw fetches diff, analyzes code, sends review

Step 1: Mac Mini M4 as Self-Hosted Runner

You register the Mac Mini as a self-hosted runner in your repo settings under Settings → Actions → Runners → New self-hosted runner, choose macOS + ARM64, and run the install commands. The runner sits idle until a workflow targets it via runs-on: self-hosted. Macly offers managed M4 Mac Mini runners that claim 5x faster builds than GitHub-hosted macOS runners if you don't want to manage the hardware yourself. macly

One thing to note: starting March 16, 2026, GitHub requires self-hosted runners to be at least v2.329.0 or they'll be blocked from picking up jobs. github

Step 2: Workflow Triggers OpenClaw

The workflow file is simple — on PR open, run OpenClaw on the self-hosted runner. OpenClaw uses gh CLI commands to interact with GitHub, and this is the recommended approach: zenvanriel

• gh pr view — fetches the diff, description, comments, review status
• gh issue view — pulls related issue context
• gh run view — checks CI status and failure logs
• gh api — escape hatch for anything else

OpenClaw handles the analysis and sends results wherever you want — Telegram, Slack, or back to the PR as a comment. zenvanriel

Step 3: What OpenClaw Actually Does

The practical pattern from teams already doing this: pub.towardsai

• PR review — fetches the diff, checks for missing tests, unclear naming, security concerns, inconsistencies with project conventions, generates a detailed summary
• CI failure diagnosis — when a workflow fails, OpenClaw fetches logs, parses errors, identifies likely causes, and alerts the developer who pushed the commit before they even check the Actions tab zenvanriel
• Issue triage — scans new issues, classifies them (bug, feature, performance), matches against existing open issues, suggests assignment
• Release notes drafting — aggregates merged PRs since last tag, generates changelog

The Key Design Decision: Read-Only

The smartest teams keep OpenClaw read-only on the repo. It can fetch anything via gh, but it never approves, merges, or pushes. It sends its analysis to humans (via Telegram, Slack, or PR comments), and humans make the final call. This eliminates the risk of an AI agent merging bad code or applying incorrect labels. It's the "pair programmer, not autonomous decision maker" pattern. zenvanriel

Why the Mac Mini M4 Specifically Matters

This is where your setup gets interesting compared to just using GitHub-hosted runners:

• OpenClaw + Ollama runs locally — your code review agent can use a local 30B model, so your proprietary code never leaves your machine. No API calls to OpenAI or Anthropic with your codebase in the prompt. meyer-laurent
• Stateful environment — unlike ephemeral GitHub-hosted runners that spin up fresh each time, your Mac Mini persists. OpenClaw's memory stays intact between runs, so it learns your project's conventions over time. meyer-laurent
• Cost — GitHub-hosted macOS runners are $0.16/minute (10x Linux pricing). A Mac Mini M4 is a one-time ~$600–$900 purchase that pays for itself in weeks if you run frequent CI. youtube
• Unified memory — the M4's 24–32GB unified RAM means OpenClaw + Ollama + your build tools all share the same memory pool efficiently. No GPU memory bottleneck. macly

A Practical Workflow YAML Sketch

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  ai-review:
    runs-on: self-hosted  # your Mac Mini M4
    steps:
      - uses: actions/checkout@v4
      - name: Run OpenClaw PR Review
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          # OpenClaw analyzes the PR via gh CLI
          openclaw run --skill pr-review \
            --context "$(gh pr view ${{ github.event.number }} --json body,diff)" \
            --notify telegram

This is basically the architecture GitHub Copilot's own coding agent uses — it added self-hosted runner support in October 2025, confirming the pattern is production-ready. You'd just be swapping Copilot for OpenClaw + a local model, which gives you full control and privacy. github

Given the Tauri app you're building, this could be a natural extension — your Mac Mini becomes both your CI runner and your local AI code reviewer, with the review results feeding back into your development workflow.