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Agent Skill · 开发

ralph-wiggum(拉尔夫·维格姆)

ralph-wiggum 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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-wiggum 是开发类 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,第三方扫描有未通过项,按安装量在开发类的 21,746 个 Skill 里排第 13,537。

总安装 879周安装 37GitHub Stars 442下载 308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-wiggum(拉尔夫·维格姆)
来源仓库:https://github.com/agrimsingh/ralph-wiggum-cursor
仓库路径:skills/ralph-wiggum
安装命令:
npx skills add https://github.com/agrimsingh/ralph-wiggum-cursor --skill ralph-wiggum
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/agrimsingh/ralph-wiggum-cursor --skill ralph-wiggum

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。

用于处理 GitHub 仓库、Issue、Pull Request 等协作信息。适合在代码审查、分支管理和团队协作中整理变更内容。可结合仓库状态快速定位问题或生成提交摘要。ralph-wiggum 属于开发类 Skill,可作为该场景下的辅助能力补充。安装前需确认是否有网络访问、文件读写和执行命令权限。

注意维护状态,避免对只读或保护分支进行误操作。

来源仓库内路径:skills/ralph-wiggum

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/agrimsingh/ralph-wiggum-cursor --skill ralph-w…;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-wiggum 怎么安装?

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

ralph-wiggum 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-wiggum 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-wiggum 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-wiggum 用的人多吗?

公开数据是累计安装 879 次,近一周安装 37 次,下载 308 次,GitHub 442 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-wiggum 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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

  • 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 Codex,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

  • 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-wiggum

拉尔夫·维格姆 · 本页

879442需要联网MIT未通过

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 Wiggum: Autonomous Iteration with Context Engineering

Ralph is a technique for autonomous AI development. In its purest form, Ralph is a loop that repeatedly feeds the same prompt to an AI agent, letting it iterate on a task until completion. The key insight is that context is like memory - when you malloc() data into the context window, it cannot be free()'d except by starting fresh.

Core Philosophy

"That's the beauty of Ralph - the technique is deterministically bad in an undeterministic world."

Ralph will make mistakes. That's expected. Each mistake is an opportunity to add a "sign" (guardrail) that prevents that mistake in the future. Like tuning a guitar, you adjust Ralph until it plays the right notes.

The malloc/free Metaphor

  • Context is memory: Everything loaded into the LLM's context window stays there
  • You cannot free() context: The only way to clear context is to start a new conversation
  • One task per context: Mixed concerns lead to autoregressive failure
  • Don't redline: Pushing context to limits degrades performance
  • Gutter detection: Once the bowling ball is in the gutter, start fresh

How This Skill Works

State Files (The Persistent Memory)

Ralph tracks state in files, NOT in context:

.ralph/
├── state.md           # Current iteration, task, completion criteria
├── guardrails.md      # Accumulated "signs" from observed failures
├── context-log.md     # What's been loaded into context
├── failures.md        # Failure patterns for learning
└── progress.md        # What's been accomplished

The Iteration Cycle

  1. Read state files to understand current task and progress
  2. Check guardrails for relevant "signs" to follow
  3. Work on the task - implement, test, refine
  4. Update progress in files (not just context)
  5. Commit checkpoint via git
  6. Evaluate completion against criteria
  7. If not complete: Signal for next iteration
  8. If stuck: Detect gutter, suggest fresh context

Guardrails ("Signs")

When Ralph makes a mistake, add a sign:

## Sign: Don't Jump Off The Slide
- **Trigger**: When implementing authentication
- **Instruction**: Always validate tokens before trusting claims
- **Added after**: Iteration 5 - security vulnerability introduced

Signs accumulate in guardrails.md and are injected into future iterations.

Usage

Starting a Ralph Loop

Create a RALPH_TASK.md file in your project root:

---
task: Build a REST API for task management
completion_criteria:
  - All CRUD endpoints working
  - Input validation implemented
  - Tests passing with >80% coverage
  - API documentation complete
max_iterations: 50
---

## Requirements

Build a task management API with the following endpoints:
- POST /tasks - Create a task
- GET /tasks - List all tasks
- GET /tasks/:id - Get a task
- PUT /tasks/:id - Update a task
- DELETE /tasks/:id - Delete a task

## Constraints

- Use TypeScript
- Use Express.js
- Use SQLite for storage
- Follow REST conventions

Then tell Cursor: "Start a Ralph loop on this task"

Monitoring Progress

Check .ralph/progress.md to see what's been accomplished:

## Iteration 1
- Created project structure
- Implemented POST /tasks endpoint
- Status: Partial progress

## Iteration 2
- Added GET endpoints
- Fixed validation bug
- Status: Continuing

When to Start Fresh

Ralph will detect "gutter" situations:

  • Same error repeated 3+ times
  • Context approaching limits
  • Circular failure patterns

When detected, Ralph will suggest: "Context is polluted. Recommend starting fresh conversation."

Best Practices

1. Clear Completion Criteria

❌ Bad: "Make a good API" ✅ Good: "All tests passing, coverage >80%, docs complete"

2. Incremental Goals

❌ Bad: "Build complete e-commerce platform" ✅ Good: Phase 1: Auth, Phase 2: Products, Phase 3: Cart

3. Let Failures Teach

Don't intervene too quickly. Let Ralph fail, then add signs.

4. Trust the Files

Progress is in files and git, not in your head or the context.

5. Fresh Context is Cheap

Don't hesitate to start fresh. State persists in files.

Integration with Cursor Hooks

This skill uses Cursor hooks for:

  • beforeSubmitPrompt: Inject guardrails and context awareness
  • beforeReadFile: Track context allocations
  • afterFileEdit: Update progress tracking
  • stop: Evaluate completion, trigger next iteration or fresh start

See scripts/ for hook implementations.

Learn More

平台分布

Claude Code

29.68%
按下载量换算91

OpenCode

20.82%
按下载量换算64

Gemini CLI

18.4%
按下载量换算57

Antigravity

12.25%
按下载量换算38

Codex

8.13%
按下载量换算25

Cursor

3.54%
按下载量换算11

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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