I wanted a bot that can do real engineering work end-to-end: watch GitHub comments, generate safe responses, and keep the team informed in Slack without creating noisy loops.
The goal
The target behavior is simple: when someone pings the assistant in a GitHub discussion, issue, or PR comment, it should evaluate the request, draft a response through OpenClaw, post back to GitHub, and notify Slack with context and a link.
This is based on my GitHub Comment Watcher template, which is structured as an operations-first starting point rather than a demo script.
How the automation loop works
- Poll recent GitHub comment events (Discussions, Issues, PR comments, PR reviews).
- Filter by trigger policy (mentions or slash commands).
- Skip bot-authored events to prevent self-reply loops.
- Enforce a rolling 24-hour quota for reply volume control.
- Send prompt context to OpenClaw for response generation.
- Post reply back to GitHub via
gh. - Send Slack notification with outcome and permalink.
Safety controls that matter in production
- Loop prevention: explicit bot identity checks plus
ALLOW_SELF_TRIGGER=false. - Rate limiting: global rolling quota (default 20 replies/24h) across watchers.
- Dry run mode: validate triggers and prompts before posting.
- Credential hygiene: no real tokens in repo; least-privilege GitHub and Slack scopes.
What this unlocks for teams
You get a practical assistant that lives where engineers already work. GitHub stays the system of record, Slack gets transparent operational updates, and OpenClaw handles the reasoning and response generation path.
The key is not full autonomy on day one. The key is controlled autonomy with clear triggers, quotas, and observability.
Quick start checklist
- Clone template and copy env examples.
- Configure
OWNER/REPO, OpenClaw profile, trigger policy, and Slack destination. - Verify dependencies:
gh,jq,node,curl. - Run both watchers with
DRY_RUN=true. - Deploy with systemd timer for regular polling cycles.
