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monitored-ralph-loop受监控的拉尔夫循环

Agent Skill

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。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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 concernspecs/*.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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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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

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