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Agent Skill · 研究检索

monitored-ralph-loop(受监控的拉尔夫循环)

monitored-ralph-loop 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

monitored-ralph-loop 是研究检索类 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,按安装量在研究检索类的 73,675 个 Skill 里排第 3,733。

总安装 40,368周安装 1,682GitHub Stars 公开资料未说明下载 13,456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
0.1.1

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:monitored-ralph-loop(受监控的拉尔夫循环)
来源仓库:https://github.com/endogen/monitored-ralph-loop
安装命令:
openclaw skills install monitored-ralph-loop
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 monitored-ralph-loop

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

为 Ralph Wiggum/AI 代理循环生成复制粘贴 bash 脚本(Codex、Claude Code、OpenCode、Goose)。

当要求“Ralph 循环”、“Ralph Wiggum 循环”或 AI 循环时使用,通过 PROMPT.md + AGENTS.md、SPECS 和 IMPLMENTATION_PLAN.md 来规划/构建代码,包括规划与构建模式、背压、沙箱和完成条件。

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 monitored-ralph-loop;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

monitored-ralph-loop 怎么安装?

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

monitored-ralph-loop 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。

monitored-ralph-loop 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 monitored-ralph-loop 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。

monitored-ralph-loop 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。

monitored-ralph-loop 用的人多吗?

公开数据是累计安装 40,368 次,近一周安装 1,682 次,下载 13,456 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。

monitored-ralph-loop 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
monitored-ralph-loop

受监控的拉尔夫循环 · 本页

40,368—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
Generate copy-paste bash scripts for Ralph Wiggum/AI agent loops (Codex, Claude Code, OpenCode, Goose). Use when asked for a "Ralph loop", "Ralph Wiggum loop", or an AI loop to plan/build code via PROMPT.md + AGENTS.md, SPECS, and IMPLEMENTATION_PLAN.md, including PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions.

Ralph Loop (Event-Driven)

Enhanced Ralph pattern with event-driven notifications — Codex/Claude calls OpenClaw when it needs attention instead of polling.

Key Concepts

Clean Sessions

Each iteration spawns a fresh agent session with clean context. This is intentional:

  • Avoids context window limits
  • Each codex exec is a new process with no memory of previous runs
  • Memory persists via files: IMPLEMENTATION_PLAN.md, AGENTS.md, git history

File-Based Notification Fallback

If OpenClaw is rate-limited when Codex sends a wake notification:

  1. The notification is written to .ralph/pending-notification.txt
  2. Wake is attempted (may fail)
  3. When OpenClaw recovers, it checks for pending notifications
  4. Work is never lost — it's all in git/files

File Structure

project/
├── PROMPT.md                      # Loaded each iteration (mode-specific)
├── AGENTS.md                      # Project context, test commands, learnings
├── IMPLEMENTATION_PLAN.md         # Task list with status
├── specs/                         # Requirements specs
│   ├── overview.md
│   └── <feature>.md
└── .ralph/
    ├── ralph.log                  # Execution log
    ├── pending-notification.txt   # Current pending notification (if any)
    └── last-notification.txt      # Previous notification (for reference)

Notification Format

.ralph/pending-notification.txt:

{
  "timestamp": "2026-02-07T02:30:00+01:00",
  "project": "/home/user/my-project",
  "message": "DONE: All tasks complete.",
  "iteration": 15,
  "max_iterations": 20,
  "cli": "codex",
  "status": "pending"
}

Status values:

  • pending — Wake failed or not attempted
  • delivered — Wake succeeded

OpenClaw Recovery Procedure

When coming back online after rate limit or downtime, check for pending notifications:

# Find all pending notifications across projects
find ~/projects -name "pending-notification.txt" -path "*/.ralph/*" 2>/dev/null

# Or check a specific project
cat /path/to/project/.ralph/pending-notification.txt

Recovery Actions by Message Prefix

PrefixAction
DONE:Report completion to user, summarize what was built
PLANNING_COMPLETE:Inform user, ask if ready for BUILDING mode
PROGRESS:Log it, update user if significant
DECISION:Present options to user, wait for answer, inject into AGENTS.md
ERROR:Check logs (.ralph/ralph.log), analyze, help or escalate
BLOCKED:Escalate to user immediately with full context
QUESTION:Present to user, get clarification, inject into AGENTS.md

Injecting Responses

To answer a decision/question for the next iteration:

echo "## Human Decisions
- [$(date '+%Y-%m-%d %H:%M')] Q: <question>? A: <answer>" >> AGENTS.md

The next Codex session will read AGENTS.md and see the answer.

Clearing Notifications

After processing a notification, clear it:

mv .ralph/pending-notification.txt .ralph/last-notification.txt

Workflow

1. Collect Requirements

Ask for (if not provided):

  • Goal/JTBD: What outcome is needed?
  • CLI: codex, claude, opencode, goose
  • Mode: PLANNING, BUILDING, or BOTH
  • Tech stack: Language, framework, database
  • Test command: How to verify correctness
  • Max iterations: Default 20

2. Generate Specs

Break the goal into topics of concern → specs/*.md:

# specs/overview.md
## Goal
<one-sentence JTBD>

## Tech Stack
- Language: Python 3.11
- Framework: FastAPI
- Database: SQLite
- Frontend: HTMX + Tailwind

## Success Criteria
- [ ] Criterion 1
- [ ] Criterion 2

3. Generate AGENTS.md

# AGENTS.md

## Project
<brief description>

## Commands
- **Install**: `pip install -e .`
- **Test**: `pytest`
- **Lint**: `ruff check .`
- **Run**: `python -m app`

## Backpressure
Run after each implementation:
1. `ruff check . --fix`
2. `pytest`

## Human Decisions
<!-- Decisions made by humans are recorded here -->

## Learnings
<!-- Agent appends operational notes here -->

4. Generate PROMPT.md (Mode-Specific)

PLANNING Mode
# Ralph PLANNING Loop

## Goal
<JTBD>

## Context
- Read: specs/*.md
- Read: Current codebase structure
- Update: IMPLEMENTATION_PLAN.md

## Rules
1. Do NOT implement code
2. Do NOT commit
3. Analyze gaps between specs and current state
4. Create/update IMPLEMENTATION_PLAN.md with prioritized tasks
5. Each task should be small (< 1 hour of work)
6. If requirements are unclear, list questions

## Notifications
When you need input or finish planning:

openclaw gateway wake --text "PLANNING: <your message>" --mode now


Use prefixes:
- `DECISION:` — Need human input on a choice
- `QUESTION:` — Requirements unclear
- `DONE:` — Planning complete

## Completion
When plan is complete and ready for building, add to IMPLEMENTATION_PLAN.md:

STATUS: PLANNING_COMPLETE

Then notify:

openclaw gateway wake --text "DONE: Planning complete. X tasks identified." --mode now

BUILDING Mode
# Ralph BUILDING Loop

## Goal
<JTBD>

## Context
- Read: specs/*.md, IMPLEMENTATION_PLAN.md, AGENTS.md
- Implement: One task per iteration
- Test: Run backpressure commands from AGENTS.md

## Rules
1. Pick the highest priority incomplete task from IMPLEMENTATION_PLAN.md
2. Investigate relevant code before changing
3. Implement the task
4. Run backpressure commands (lint, test)
5. If tests pass: commit with clear message, mark task done
6. If tests fail: try to fix (max 3 attempts), then notify
7. Update AGENTS.md with any operational learnings
8. Update IMPLEMENTATION_PLAN.md with progress

## Notifications
Call OpenClaw when needed:

openclaw gateway wake --text "<PREFIX>: <message>" --mode now


Prefixes:
- `DECISION:` — Need human input (e.g., "SQLite vs PostgreSQL?")
- `ERROR:` — Tests failing after 3 attempts
- `BLOCKED:` — Missing dependency, credentials, or unclear spec
- `PROGRESS:` — Major milestone complete (optional)
- `DONE:` — All tasks complete

## Completion
When all tasks are done:
1. Add to IMPLEMENTATION_PLAN.md: `STATUS: COMPLETE`
2. Notify:

openclaw gateway wake --text "DONE: All tasks complete. Summary: <what was built>" --mode now

5. Run the Loop

Use the provided scripts/ralph.sh:

# Default: 20 iterations with Codex
./scripts/ralph.sh 20

# With Claude Code
RALPH_CLI=claude ./scripts/ralph.sh 10

# With tests
RALPH_TEST="pytest" ./scripts/ralph.sh

Parallel Execution

For independent tasks, use git worktrees:

# Create worktrees for parallel work
git worktree add /tmp/task-auth main
git worktree add /tmp/task-upload main

# Spawn parallel sessions (each is clean/fresh)
exec pty:true background:true workdir:/tmp/task-auth command:"codex exec --full-auto 'Implement user authentication...'"
exec pty:true background:true workdir:/tmp/task-upload command:"codex exec --full-auto 'Implement image upload...'"

Track sessions:

Session IDWorktreeTaskStatus
abc123/tmp/task-authAuth modulerunning
def456/tmp/task-uploadImage uploadrunning

Each Codex notifies independently. Check .ralph/pending-notification.txt in each worktree.


CLI-Specific Notes

Codex

  • Requires git repository
  • Each codex exec is a fresh session — no memory between calls
  • --full-auto: Auto-approve in workspace (sandboxed)
  • --yolo: No sandbox, no approvals (dangerous but fast)
  • Default model: gpt-5.2-codex

Claude Code

  • --dangerously-skip-permissions: Auto-approve (use in sandbox)
  • No git requirement
  • Each invocation is fresh

OpenCode

  • opencode run "$(cat PROMPT.md)"

Goose

  • goose run "$(cat PROMPT.md)"

Safety

⚠️ Auto-approve flags are dangerous. Always:

  1. Run in a dedicated directory/branch
  2. Use a sandbox (Docker/VM) for untrusted projects
  3. Have git reset --hard ready as escape hatch
  4. Review commits before pushing

Quick Start

# 1. Create project directory
mkdir my-project && cd my-project && git init

# 2. Copy templates from skill
cp /path/to/ralph-loop/templates/* .
mv PROMPT-PLANNING.md PROMPT.md

# 3. Create specs
mkdir specs
cat > specs/overview.md << 'EOF'
## Goal
Build a web app that...

## Tech Stack
- Python 3.11 + FastAPI
- SQLite
- HTMX + Tailwind

## Features
1. Feature one
2. Feature two
EOF

# 4. Edit PROMPT.md with your goal

# 5. Run the loop
./ralph.sh 20

Example: Antique Catalogue

# specs/overview.md
## Goal
Web app for cataloguing antique items with metadata, images, and categories.

## Tech Stack
- Python 3.11 + FastAPI
- SQLite + SQLAlchemy
- HTMX + Tailwind CSS
- Local file storage for images

## Features
1. CRUD for items (name, description, age, purchase info, dimensions)
2. Image upload (multiple per item)
3. Tags and categories
4. Search and filter
5. Multiple view modes (grid, list, detail)

The agent will:

  1. (PLANNING) Break this into 10-15 tasks
  2. (BUILDING) Implement each task, one per iteration
  3. Commit after each successful implementation
  4. Notify on completion or if blocked

平台分布

OpenClaw

87.95%
按下载量换算11,835

安全审计

VirusTotal

未展示

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 monitored-ralph-loop 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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