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Agent Skill · 待分类

ralph-creator(拉尔夫创造者)

ralph-creator 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-creator 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有未通过项,按安装量在待分类类的 11,209 个 Skill 里排第 9,111。

总安装 259周安装 11GitHub Stars 2下载 91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
unknown

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-creator(拉尔夫创造者)
来源仓库:https://github.com/jonmumm/skills
仓库路径:skills/ralph-creator
安装命令:
npx skills add https://github.com/jonmumm/skills --skill ralph-creator
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/jonmumm/skills --skill ralph-creator

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合围绕仓库状态、代码变更或协作事项进行整理。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,避免触发联网或命令执行。

ralph-creator 属于待分类类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ralph-creator

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/jonmumm/skills --skill ralph-creator;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

    它需要调用外部服务,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-creator 怎么安装?

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

ralph-creator 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-creator 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

ralph-creator 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ralph-creator 用的人多吗?

公开数据是累计安装 259 次,近一周安装 11 次,下载 91 次,GitHub 2 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-creator 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。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
ralph-creator

拉尔夫创造者 · 本页

2592调用外部服务未明确未通过

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 Creator

Generate AFK Ralph loop scripts. Ralph runs claude -p --dangerously-skip-permissions in a loop — one task per iteration, progress tracked between iterations, stops when all tasks are done.

Output Structure

All artifacts go in .ralph/ at the project root:

.ralph/
├── ralph-<name>.sh    # Loop script (executable)
├── backlog.md         # Task checklist
├── progress.md        # Working memory (delete after run)
└── lessons.md         # Mistakes to avoid (persists)

Workflow

1. Gather Context

Ask the user (2-3 questions max, skip if obvious from context):

  • What's the task?
  • What context files should the agent read?
  • Roughly how many subtasks? (determines iteration count)

2. Create .ralph/ Directory

mkdir -p .ralph

3. Write the Backlog

Create .ralph/backlog.md. See references/backlog-format.md.

Rules:

  • Each task: - [] checkbox, bolded title, clear description
  • One task = one iteration = one context window
  • Order by dependency, then priority
  • Enough detail for a fresh Claude instance to execute without ambiguity

4. Generate the Script

Create .ralph/ralph-<name>.sh from references/script-template.md.

Customize the placeholders:

  • {{ADDITIONAL_CONTEXT_REFS}} — extra @path refs for skills, specs, style guides
  • {{ONE_SENTENCE_DIRECTIVE}} — imperative statement: what each iteration produces
  • {{EXECUTE_STEPS}} — numbered sub-steps for the domain-specific work
  • {{VERIFY_INSTRUCTION}} — how to confirm the work is correct
  • {{ADDITIONAL_RULES}} — domain-specific constraints

CRITICAL: The @ refs and prompt text MUST be in ONE quoted string passed to claude -p. The template handles this correctly — do not separate them into different variables or CLI args.

If the task needs a style guide or reference material, put it in a SEPARATE file and add it to {{ADDITIONAL_CONTEXT_REFS}} as another @ reference. Do NOT inline it in the prompt.

5. Create Tracking Files

.ralph/progress.md:

# Progress
Working memory for ralph-<name>. Delete after run.
---

.ralph/lessons.md:

# Lessons
Patterns and mistakes. Review at start of each iteration.
---

See references/tracking-format.md for entry format.

6. Make Executable and Report

chmod +x .ralph/ralph-<name>.sh

Tell the user the run command:

.ralph/ralph-<name>.sh <iterations>

Suggest iterations = task count + 2 (buffer for retries + final COMPLETE check).

Prompt Design Rules

  1. Imperative first line — The prompt MUST open with a direct command: YOUR JOB: <one sentence>. The model reads @ context files first (often 1000+ lines), so the prompt must immediately snap it into execution mode, not summary mode.
  2. Numbered steps, not section headers — Use STEPS: with numbered items (1. Do X. 2. Do Y.), NOT markdown ## Section headers. Section headers read as documentation. Numbered steps read as commands.
  3. Explicit "Do NOT" rules — End the prompt with a RULES: block that includes what the agent must NOT do (e.g., "Do NOT summarize the spec. Do NOT ask what to work on. Just execute."). Without these, the model defaults to helpful-assistant mode.
  4. Short and punchy — Prompt should be under 30 lines. The @ files provide all the context. The prompt is ONLY for instructions. If domain-specific guidance (style guides, conventions) is needed, put it in a separate @-referenced file, not inline.
  5. @ context refs — Load docs upfront, don't waste tokens exploring
  6. Verification step — Agent confirms work before marking done
  7. Quality bar — Measurable "done" criteria, no ambiguity
  8. One task per iteration — Always include: ONLY work on ONE task. Do not continue to the next.
  9. Completion sigil — Always: If all tasks are done, output <promise>COMPLETE</promise>.
  10. Tool instructions — If using MCP tools, APIs, or CLIs, include explicit how-to in the execute steps

Prompt Anti-Patterns (Avoid These)

These patterns cause the agent to summarize context instead of executing tasks:

Anti-PatternWhy It FailsFix
@ refs as separate CLI argsModel receives file contents without a directive, just summarizes them@ refs MUST be inline in the prompt string: "@file.md YOUR JOB:..."
OUTPUT=$(claude -p...) captureBuffers all output — user sees nothing for 10-20 minutesUse `\tee "$LOGFILE"` for real-time streaming + log capture
Starting with background/explanationModel treats it as conversation contextLead with YOUR JOB: imperative
## Section headers in promptReads as documentation, not commandsUse STEPS: with numbered list
Long inline style guides/conventionsBuries the actual instructionsMove to separate @-referenced file
Prompt > 40 linesModel loses the thread after heavy @ contextKeep to 15-30 lines max
No explicit "Do NOT" rulesModel defaults to helpful-assistant behaviorsAdd RULES: block with prohibitions
Sections named "Orient" / "Context"Encourages reading + summarizing, not actingName steps as actions: "Read backlog", "Write the file"

平台分布

Codex

38.25%
按下载量换算35

Claude

32.51%
按下载量换算30

Cursor

16.79%
按下载量换算15

Gemini CLI

8.7%
按下载量换算8

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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