Prompt Request Pipeline

GitHub Issue → auto-implement → PR → review → auto-merge pipeline. Write an Issue with [auto] tag, and the pipeline handles everything: task analysis, implementation, testing, PR creation, review, and merge. Includes progress comments on Issues and early-exit optimization.

Issue を書くだけで、自動実装 → PR → レビュー → マージまで全自動で回るパイプライン。

Overview

text

[auto] Issue 起票
  → 🚀 実装開始コメント
  → Phase A: タスク分解（Omega-bridge or Issue本文）
  → 📋 分析完了コメント
  → Phase B: 実装 + テスト
  → ✅ 実装完了コメント
  → Phase C: commit → push → PR作成
  → 🔗 PR作成コメント
  → 自動レビュー → 自動マージ → Issue close

Prerequisites

• OpenClaw Gateway running with hooks enabled
• GitHub CLI (gh) authenticated
• Git SSH access to target repository
• GitHub Webhook pointing to OpenClaw hooks endpoint

Setup

1. Register GitHub Webhook

On your GitHub repo → Settings → Webhooks → Add webhook:

• Payload URL: https://<your-openclaw-endpoint>/hooks/github
• Content type: application/json
• Secret: Your OpenClaw hooks token
• Events: Select individual events:
  • Issues
  • Pull requests
  • Pull request reviews
  • Check runs
  • Issue comments
  • Push

2. Configure OpenClaw hooks

Add this to your openclaw.json under hooks.mappings:

json

{
  "match": { "path": "github" },
  "action": "agent",
  "name": "GitHub",
  "sessionKey": "hook:github:{{repository.name}}:{{headers.x-github-event}}:{{issue.number}}{{pull_request.number}}{{check_run.id}}",
  "messageTemplate": "<see templates/messageTemplate.txt>",
  "deliver": true,
  "allowUnsafeExternalContent": true,
  "channel": "telegram",
  "to": "<your-chat-id>",
  "model": "anthropic/claude-opus-4-6",
  "thinking": "high",
  "timeoutSeconds": 900
}

3. Set working directory

In the messageTemplate, replace the working directory path:

• WORKDIR variable: where repositories are cloned (e.g., C:\Users\you\Dev or /home/you/dev)

4. (Optional) Omega-bridge

If you have Miyabi's omega-bridge for SWML-based task decomposition:

• Set the path to omega-bridge.ts in the messageTemplate
• If not available, the pipeline falls back to implementing directly from Issue body

Usage

Basic: Create an [auto] Issue

markdown

Title: [auto] Add utility function X

Body:
## Requirements
- Create scripts/x.sh with function do_x()
- Add tests in tests/test-x.sh

## Acceptance Criteria
- Function returns expected output
- Tests pass

Advanced: With agent personality

markdown

Title: [auto] [content] Write article about Y

Body:
Read agents/content-agent/AGENTS.md and SOUL.md first.
Follow the rules defined there.

## Topic
...

## Output
- File: articles/y.md
- Word count: 6000

Advanced: With skill reference

markdown

Title: [auto] Generate report with weather data

Body:
Read skills/weather/SKILL.md for API usage.

## Requirements
...

How It Works

Issue Events (action=opened)

1. Early exit check: If action is closed/labeled/etc → 1-line reply, stop
2. [auto] check: Title starts with [auto] or body contains <!-- auto-implement -->
3. Phase A: Task decomposition (omega-bridge or direct)
4. Phase B: Implementation (branch, code, test)
5. Phase C: Integration (commit, push, PR)
6. Progress comments posted at each phase

PR Events (action=opened/synchronize)

1. Skip bot senders (loop prevention)
2. Diff review for quality/security
3. Auto-merge if ALL conditions met:
   • PR title contains [auto] or branch starts with feature/issue-
   • Review is LGTM
   • CI checks pass (or empty = pass)
   • No merge conflicts

Safety Rules

• Never force push
• Never push directly to main
• Never run permission commands (icacls/chmod/chown)
• Max 3 CI fix retries per PR
• Bot sender events are skipped

Configuration Options

Option	Default	Description
model	claude-opus-4-6	Model for hook sessions
thinking	high	Thinking level
timeoutSeconds	900	Max execution time (15 min)
deliver	true	Send results to chat
channel	telegram	Delivery channel

Performance (measured)

Metric	Before optimization	After optimization
close/push events	8-12 min, ~500 tokens	3 sec, ~15 tokens
[auto] Issue → merged PR	N/A (stuck)	~5 min
Full pipeline (Issue → merge)	N/A	~5 min

Tips

• Keep Issues small: 1 Issue = 1 clear deliverable, ≤300 lines of diff
• Be specific: The quality of the Issue body directly determines output quality
• Use templates: Create Issue templates for recurring task types
• Reference skills: Point the agent to relevant SKILL.md files for domain knowledge
• Reference agent definitions: Store AGENTS.md/SOUL.md in the repo for consistent behavior