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Agent Skill · 开发

ralph-loop-agent(拉尔夫·洛普特工)

ralph-loop-agent 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ralph-loop-agent 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 448。

总安装 93,960周安装 3,915GitHub Stars 公开资料未说明下载 31,320核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ralph-loop-agent(拉尔夫·洛普特工)
来源仓库:https://github.com/addozhang/ralph-loop-agent
安装命令:
openclaw skills install ralph-loop-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ralph-loop-agent

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

指导 OpenClaw 代理使用 exec 和 process 工具执行 Ralph Wiggum 循环。代理通过 pty:true 协调编码代理(Codex、Claude Code、OpenCode、Goose)并提供适当的 TTY 支持。

通过 PROMPT.md + AGENTS.md、SPECS 和 IMPLMENTATION_PLAN.md 计划/构建代码。包括规划与建设模式、背压、沙箱和完成条件。用户请求循环,代理使用工具执行。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install ralph-loop-agent;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ralph-loop-agent 怎么安装?

在终端执行 openclaw skills install ralph-loop-agent。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ralph-loop-agent 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ralph-loop-agent 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install ralph-loop-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

ralph-loop-agent 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ralph-loop-agent 用的人多吗?

公开数据是累计安装 93,960 次,近一周安装 3,915 次,下载 31,320 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ralph-loop-agent 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
ralph-loop-agent

拉尔夫·洛普特工 · 本页

93,960—执行命令MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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
  1. Capture session ID from exec tool response
  1. 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)
  1. Check completion by reading IMPLEMENTATION_PLAN.md for sentinel text
  1. 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:

CLICommand String Pattern
OpenCodeopencode run --model <MODEL> "$(cat PROMPT.md)"
Codexcodex exec <FLAGS> "$(cat PROMPT.md)" (requires git)
Claude Codeclaude <FLAGS> "$(cat PROMPT.md)"
Pipi --provider <PROVIDER> --model <MODEL> -p "$(cat PROMPT.md)"
Goosegoose 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:

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

ProblemSolution
OpenCode hangsEnsure agent uses exec tool with pty: true
Session won't startCheck CLI path, git repo, command syntax
Completion not detectedVerify sentinel format in IMPLEMENTATION_PLAN.md
Process timeoutAgent should increase timeout parameter or simplify tasks
Parallel conflictsAgent should use git worktrees for isolation
Can't see progressAgent 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.

平台分布

OpenClaw

84.47%
按下载量换算26,456

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install ralph-loop-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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