AlphaMoatによると、2026-09時点でRalph Loop (Agent Mode)のダウンロード数は4,385、スター数は0で、AIスキル全体5,078件中2,227位、AIエージェントでは969件中424位です。
| プラットフォーム | インストール数 | ダウンロード数 | スター数 | コメント |
|---|---|---|---|---|
| ClawHub ↗ | 142 | 4225 | - | 2 |
| SkillHub ↗ | 141 | 4385 | - | — |
プラットフォームごとに集計方法と集計時点が異なります。
実行・プロセスツールでコーディングエージェントを調整し、コマンド実行、ファイル編集、テスト、コミットを行う場合があります。開始前に範囲、サンドボックス、権限を確認し、コミット前に差分とテスト結果を確認してください。 Git、Bash、対応するコーディング agent CLI が必要です。権限確認の回避は避けます。
資料公開日 2026-10-29
スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。
2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。
| 月 | ダウンロード数 | 前月比 | スター数 |
|---|---|---|---|
| 2026-09 | 4385 | — | - |
| 2026-08 | 4144 | +2.7% | - |
| 2026-07 | 4035 | — | - |
| 2026-05 | 3610 | — | - |
| 2026-03 | 2701 | — | - |
作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。
--- name: ralph-loop description: Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agents execute using tools. version: 1.1.0 author: OpenClaw Community keywords: [ralph-loop, ai-agent, coding-agent, pty, tty, automation, loop, opencode, codex, claude, goose, exec-tool, process-tool] license: MIT --- # Ralph Loop
## Overview
This skill guides **OpenClaw agents** to execute Ralph Loop workflows using the `exec` and `process` tools. The agent orchestrates AI coding agent sessions following the Ralph playbook flow:
1) **Define Requirements** → JTBD → Focus Topics → `specs/*.md`
2) **PLANNING Loop** → Create/update `IMPLEMENTATION_PLAN.md` (do not implement)
3) **BUILDING Loop** → Implement tasks, run tests (backpressure), update plan, commit
The loop persists context via `PROMPT.md` + `AGENTS.md` (loaded each iteration) and the plan/specs on disk.
## How This Skill Works
This skill generates instructions for **OpenClaw agents** to execute Ralph Loops using the `exec` and `process` tools.
- The agent calls `exec` tool with the coding agent command
- Uses `pty: true` to provide TTY for interactive CLIs
- Uses `background: true` for monitoring capabilities
- Uses `process` tool to monitor progress and detect completion
**Important**: Users don't run these scripts directly - the OpenClaw agent executes them using its tool capabilities.
---
## TTY Requirements
Some coding agents **require a real terminal (TTY)** to work properly, or they will hang:
**Interactive CLIs (need TTY)**:
- OpenCode, Codex, Claude Code, Pi, Goose
**Non-interactive CLIs (file-based)**:
- aider, custom scripts
**Solution**: Use **exec + process mode** for interactive CLIs, simple loops for file-based tools.
---
## Agent Tool Usage Patterns
### Interactive CLIs (Recommended Pattern)
For OpenCode, Codex, Claude Code, Pi, and Goose - these require TTY support:
**When I (the agent) receive a Ralph Loop request, I will:**
1. **Use exec tool** to launch the coding agent:
```
exec tool with parameters:
- command: "opencode run --model <MODEL> \"$(cat PROMPT.md)\""
- workdir: <project_path>
- background: true
- pty: true
- yieldMs: 60000
- timeout: 3600
```
2. **Capture session ID** from exec tool response
3. **Use process tool** to monitor:
```
process tool with:
- action: "poll"
- sessionId: <captured_session_id>
process tool with:
- action: "log"
- sessionId: <captured_session_id>
- offset: -30 (for recent output)
```
4. **Check completion** by reading `IMPLEMENTATION_PLAN.md` for sentinel text
5. **Clean up** with process kill if needed:
```
process tool with:
- action: "kill"
- sessionId: <session_id>
```
**Benefits**: TTY support, real-time logs, timeout handling, parallel sessions, workdir isolation
---
## Agent Workflow
### 1) Gather Inputs
**Required**:
- Goal / JTBD
- CLI (`opencode`, `codex`, `claude`, `goose`, `pi`, other)
- Mode (`PLANNING`, `BUILDING`, or `BOTH`)
- Max iterations (default: PLANNING=5, BUILDING=10)
**Optional**:
- Completion sentinel (default: `STATUS: COMPLETE` in `IMPLEMENTATION_PLAN.md`)
- Working directory (default: `$PWD`)
- Timeout per iteration (default: 3600s)
- Sandbox choice
- Auto-approval flags (`--full-auto`, `--yolo`, `--dangerously-skip-permissions`)
**Auto-detect**:
- If CLI in interactive list → use exec tool with pty: true
- Extract model flag from CLI requirements
### 2) Requirements → Specs (Optional)
If requirements are unclear:
- Break JTBD into focus topics
- Draft `specs/<topic>.md` for each
- Keep specs short and testable
### 3) PROMPT.md + AGENTS.md
**PROMPT.md** references:
- `specs/*.md`
- `IMPLEMENTATION_PLAN.md`
- Relevant project files
**AGENTS.md** includes:
- Test commands (backpressure)
- Build/run instructions
- Operational learnings
### 4) Prompt Templates
**PLANNING Prompt** (no implementation):
```
You are running a Ralph PLANNING loop for this goal: <goal>.
Read specs/* and the current codebase. Only update IMPLEMENTATION_PLAN.md.
Rules:
- Do not implement
- Do not commit
- Create a prioritized task list
- Write down questions if unclear
Completion:
When plan is ready, add: STATUS: PLANNING_COMPLETE
```
**BUILDING Prompt**:
```
You are running a Ralph BUILDING loop for this goal: <goal>.
Context: specs/*, IMPLEMENTATION_PLAN.md, AGENTS.md
Tasks:
1) Pick the most important task
2) Investigate code
3) Implement
4) Run backpressure commands from AGENTS.md
5) Update IMPLEMENTATION_PLAN.md
6) Update AGENTS.md with learnings
7) Commit with clear message
Completion:
When all done, add: STATUS: COMPLETE
```
### 5) CLI Command Reference
The agent constructs command strings using these patterns:
| CLI | Command String Pattern |
|-----|----------------------|
| **OpenCode** | `opencode run --model <MODEL> "$(cat PROMPT.md)"` |
| **Codex** | `codex exec <FLAGS> "$(cat PROMPT.md)"` (requires git) |
| **Claude Code** | `claude <FLAGS> "$(cat PROMPT.md)"` |
| **Pi** | `pi --provider <PROVIDER> --model <MODEL> -p "$(cat PROMPT.md)"` |
| **Goose** | `goose run "$(cat PROMPT.md)"` |
Common flags:
- Codex: `--full-auto`, `--yolo`, `--model <model>`
- Claude: `--dangerously-skip-permissions`
---
## Detailed Agent Tool Usage Examples
### Example 1: OpenCode Ralph Loop
**Agent executes this sequence:**
```
Step 1: Launch OpenCode with exec tool
{
command: "opencode run --model github-copilot/claude-opus-4.5 \"$(cat PROMPT.md)\"",
workdir: "/path/to/project",
background: true,
pty: true,
timeout: 3600,
yieldMs: 60000
}
Step 2: Capture session ID from response
sessionId: "abc123"
Step 3: Monitor with process tool every 10-30 seconds
{
action: "poll",
sessionId: "abc123"
}
Step 4: Check recent logs
{
action: "log",
sessionId: "abc123",
offset: -30
}
Step 5: Read IMPLEMENTATION_PLAN.md to check for completion
- Look for: "STATUS: COMPLETE" or "STATUS: PLANNING_COMPLETE"
Step 6: If complete or timeout, cleanup
{
action: "kill",
sessionId: "abc123"
}
```
### Example 2: Codex with Full Auto
**Agent tool calls:**
```
exec tool:
{
command: "codex exec --full-auto --model anthropic/claude-opus-4 \"$(cat PROMPT.md)\"",
workdir: "/path/to/project",
background: true,
pty: true,
timeout: 3600
}
# Then monitor with process tool as above
```
---
## Completion Detection
Use flexible regex to match variations:
```bash
grep -Eq "STATUS:?\s*(PLANNING_)?COMPLETE" IMPLEMENTATION_PLAN.md
```
**Matches**:
- `STATUS: COMPLETE`
- `STATUS:COMPLETE`
- `STATUS: PLANNING_COMPLETE`
- `## Status: PLANNING_COMPLETE`
---
## Safety & Safeguards
### Auto-Approval Flags (Risky!)
- Codex: `--full-auto` (sandboxed, auto-approve) or `--yolo` (no sandbox!)
- Claude: `--dangerously-skip-permissions`
- **Recommendation**: Use sandboxes (docker/e2b/fly) and limited credentials
### Escape Hatches
- Stop: `Ctrl+C`
- Kill session: process tool with action: "kill"
- Rollback: `git reset --hard HEAD~N`
### Best Practices
1. **Start small**: Test with 1-2 iterations first
2. **Workdir isolation**: Prevent reading unrelated files
3. **Set timeouts**: Default 1h may not fit all tasks
4. **Monitor actively**: Check logs, don't terminate prematurely
5. **Requirements first**: Clear specs before building
6. **Backpressure early**: Add tests from the start
---
## Troubleshooting
| Problem | Solution |
|---------|----------|
| OpenCode hangs | Ensure agent uses exec tool with pty: true |
| Session won't start | Check CLI path, git repo, command syntax |
| Completion not detected | Verify sentinel format in IMPLEMENTATION_PLAN.md |
| Process timeout | Agent should increase timeout parameter or simplify tasks |
| Parallel conflicts | Agent should use git worktrees for isolation |
| Can't see progress | Agent should use process tool with action: "log" |
---
## License
MIT
## Credits
This skill builds upon work by:
- **@jordyvandomselaar** - Original Ralph Loop concept and workflow design
- **@steipete** - Coding agent patterns and exec/process tool usage with pty support
Key improvement: Uses OpenClaw's `exec` tool with `pty: true` to provide TTY for interactive CLIs, solving the hanging issue that occurs with simple background bash execution.
開発中の学び、エラー、訂正を Markdown ファイルに記録し、再利用やワークスペースの記憶への昇格に備えます。
やりたいことを自然文で伝えると、ローカルとリモートのカタログからスキルを探して推薦するスキルです。
AI エージェント向けのブラウザ自動化コマンドラインスキルです。
利用者の訂正と自己点検を層構造の記憶に蓄え、使われ方に応じて昇格や保管を行うスキルです。
第三者のスキルを入れる前に、出所と権限と危険な作りを手順に沿って調べるスキルです。
エージェントの記憶とスキル間の状態を、型と制約を持つ知識グラフとして保存するスキルです。
AI エージェントの先回り、作業記憶、振り返りの手順を計画するためのワークスペース設定ガイドです。
効果的な Claude スキルの作成・更新ガイドです。
データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。