Token导航 LogoToken导航

Agent Skill · 研究检索

ralph-loop-guide(拉尔夫循环指南)

ralph-loop-guide 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 Codex、Claude、Cursor、Gemini CLI 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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-guide 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 64,068。

总安装 306周安装 13GitHub Stars 134下载 107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

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-guide(拉尔夫循环指南)
来源仓库:https://github.com/anton-abyzov/specweave
仓库路径:skills/ralph-loop-guide
安装命令:
npx skills add https://github.com/anton-abyzov/specweave --skill ralph-loop-guide
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/anton-abyzov/specweave --skill ralph-loop-guide

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。

用于记录任务执行中的错误、用户纠正和经验缺口。适合让 Agent 持续沉淀问题、修正和最佳实践。可结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态,避免触发联网或文件读写。ralph-loop-guide 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ralph-loop-guide

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/anton-abyzov/specweave --skill ralph-loop-guid…;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

    它需要需要联网,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-guide 怎么安装?

在终端执行 npx skills add https://github.com/anton-abyzov/specweave --skill ralph-loop-guide。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ralph-loop-guide 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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-guide 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

ralph-loop-guide 是免费的吗?

来源标注的许可证是 MIT。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-guide 用的人多吗?

公开数据是累计安装 306 次,近一周安装 13 次,下载 107 次,GitHub 134 星。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-guide 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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 Codex,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

  • 要求有安全扫描结论才肯安装的团队。
  • 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
ralph-loop-guide

拉尔夫循环指南 · 本页

306134需要联网MIT未收录

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

Ralph Loop Best Practices Guide

This skill provides guidelines for effective autonomous iteration using the Ralph Wiggum pattern, based on Anthropic's official ralph-loop plugin.

How Ralph Loop Works

The Ralph Loop is a methodology for iterative AI development through self-referential feedback loops:

  1. The prompt never changes between iterations
  2. Claude's previous work persists in files
  3. Each cycle, the AI sees modified files and git history
  4. Enables autonomous refinement without manual re-prompting

Effective Ralph Prompt Design

Required Elements

  1. Clear Completion Signal ## Completion Criteria - [] All unit tests pass (npm test) - [] Build succeeds (npm run build) - [] Coverage ≥80% (npm run coverage) - [] No TypeScript errors (npx tsc --noEmit) When ALL criteria are met, output: "RALPH_COMPLETE: All tasks done"
  2. Incremental Phases ## Implementation Phases ### Phase 1: Foundation - Create database schema - Set up repository layer - Write unit tests for repository ### Phase 2: Business Logic - Implement service layer - Add validation - Write service tests ### Phase 3: API Layer - Create endpoints - Add input validation - Write API tests - Run full test suite
  3. Self-Correction Cycles ` ## On Each Iteration 1. Run tests: npm test 2. If tests fail: - Read error messages - Fix the failing code - Run tests again 3. If tests pass: - Check coverage: npm run coverage - If coverage < 80%, add more tests - If coverage ≥ 80%, proceed to next phase `
  4. Safety Limits ## Safety Rules - Maximum 100 iterations per phase - If stuck for 5 iterations on same error, ask for help - Never delete test files - Always commit working state before major changes

Good Ralph Prompts

Example 1: API Development

# Task: Build User Authentication API

## Context
- Node.js + Express + TypeScript
- PostgreSQL with Prisma
- JWT authentication

## Phases

### Phase 1: Database (iterations 1-10)
Create Prisma schema for User model with:
- id, email (unique), passwordHash, createdAt, updatedAt

Run: `npx prisma migrate dev`
Test: Schema validates with `npx prisma validate`

### Phase 2: Repository (iterations 11-25)
Create UserRepository with:
- create(email, password) → User
- findByEmail(email) → User | null
- findById(id) → User | null

Tests: All repository tests pass

### Phase 3: Service (iterations 26-45)
Create AuthService with:
- register(email, password) → { user, token }
- login(email, password) → { user, token }
- validateToken(token) → User

Tests: All service tests pass

### Phase 4: Routes (iterations 46-70)
Create routes:
- POST /auth/register
- POST /auth/login
- GET /auth/me (protected)

Tests: All API tests pass with supertest

### Phase 5: Integration (iterations 71-100)
- Full E2E flow works
- Error handling for all edge cases
- Rate limiting on auth endpoints

## Completion
When `npm test` passes with 0 failures AND coverage ≥80%,
output: "RALPH_COMPLETE"

Example 2: Refactoring Task

# Task: Refactor Legacy Auth Module

## Current State
- Monolithic auth.js with 500 lines
- No tests
- Mixed concerns

## Target State
- Separate files: auth-service.ts, user-repository.ts, token-utils.ts
- 100% backward compatible
- 80%+ test coverage

## Iteration Loop

1. Read current auth.js
2. Identify one function to extract
3. Create new file with extracted function
4. Update imports in auth.js
5. Run existing integration tests
6. If tests fail, fix and retry
7. If tests pass, proceed to next function

## Completion Criteria
- [ ] auth.js < 100 lines
- [ ] All functions have dedicated files
- [ ] All tests pass
- [ ] No breaking changes to API

Output "RALPH_COMPLETE" when done.

When Ralph Works

Task TypeSuitabilityReason
API development✅ ExcellentClear test-driven feedback
Refactoring✅ ExcellentTests verify each step
Bug fixing✅ GoodReproduce → fix → verify cycle
Test writing✅ GoodCoverage metrics as feedback
Documentation⚠️ LimitedNo automated verification
UI development⚠️ LimitedVisual verification hard
Design decisions❌ PoorRequires human judgment
One-time scripts❌ PoorNo iteration benefit

When NOT to Use Ralph

  1. Subjective decisions - No objective completion signal
  2. One-time operations - No benefit from iteration
  3. Ambiguous requirements - Will spin without progress
  4. Security-critical code - Needs human review
  5. Production deployments - Too risky for autonomous action

Anti-Patterns to Avoid

1. Vague Completion Criteria

# BAD
Complete when the code looks good.

# GOOD
Complete when:
- npm test exits with code 0
- npm run build succeeds
- No TypeScript errors (npx tsc --noEmit)

2. No Phase Boundaries

# BAD
Build the entire application.

# GOOD
Phase 1: Database schema (test: migrations apply)
Phase 2: Repository layer (test: unit tests pass)
Phase 3: Service layer (test: integration tests pass)

3. Missing Error Recovery

# BAD
If something fails, figure it out.

# GOOD
If tests fail:
1. Read the error message
2. Identify the failing file:line
3. Fix the specific issue
4. Run tests again
5. If same error after 3 attempts, try alternative approach

4. No Safety Limits

# BAD
Keep going until done.

# GOOD
- Max 100 iterations total
- Max 10 retries per failing test
- Checkpoint every 25 iterations

Integration with SpecWeave

SpecWeave's /sw:auto command implements Ralph Loop with:

  1. Tasks.md as Completion Checklist

- Each [] pending task is a completion criterion - Auto mode continues until all [x] completed

  1. Built-in Quality Gates

- --build - Build must pass - --tests - Tests must pass - --e2e - E2E tests must pass - --cov N - Coverage threshold

  1. Automatic Phase Management

- Tasks grouped by User Story - Progress tracked in metadata.json - External sync keeps stakeholders informed

Real-World Success Stories

From Anthropic's documentation:

  • "6 repositories overnight"
  • "$50k contract for $297 in API costs"
  • "259 PRs, 497 commits, 40,000 lines in one month without opening IDE"

The key to success: Well-defined tasks with automated verification.

平台分布

Claude Code

26.83%
按下载量换算29

Cursor

23.93%
按下载量换算26

OpenCode

19.92%
按下载量换算21

Codex

13.78%
按下载量换算15

Antigravity

7.88%
按下载量换算8

Gemini CLI

3.86%
按下载量换算4

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

继续浏览同类 Skills