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ralph-wiggum拉尔夫·维格姆

Agent Skill

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。

总安装

879

周安装

37

GitHub Stars

442

下载量

308
CodexClaudeCursorGemini CLI

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

3

许可证

MIT

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: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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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。

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 warning/failed 结果,不能写成本站确认安全。

来源信息

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