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Agent Skill · 研究检索

ralph-orchestrator(拉尔夫·协调器)

ralph-orchestrator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

ralph-orchestrator 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,权限为操作浏览器;许可证为 MIT,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 61,288。

总安装 353周安装 15GitHub Stars 16下载 124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-orchestrator(拉尔夫·协调器)
来源仓库:https://github.com/cfircoo/claude-code-toolkit
仓库路径:skills/ralph-orchestrator
安装命令:
npx skills add https://github.com/cfircoo/claude-code-toolkit --skill ralph-orchestrator
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/cfircoo/claude-code-toolkit --skill ralph-orchestrator

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。

用于查找、检索和筛选相关信息。适合在需要根据关键词或任务场景快速定位候选结果时使用。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-orchestrator 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-orchestrator

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/cfircoo/claude-code-toolkit --skill ralph-orch…;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

    它需要操作浏览器,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-orchestrator 怎么安装?

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

ralph-orchestrator 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-orchestrator 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

ralph-orchestrator 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-orchestrator 用的人多吗?

公开数据是累计安装 353 次,近一周安装 15 次,下载 124 次,GitHub 16 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-orchestrator 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。
  • 需要处理研究检索类任务、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。

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-orchestrator

拉尔夫·协调器 · 本页

35316操作浏览器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

  1. spec-interview → Gather comprehensive requirements through guided discovery
  2. generate-prd → Create actionable Product Requirements Document
  3. ralph-convert-prd → Transform PRD into atomic user stories (prd.json)
  4. Subagent execution → Spawn ralph-coder/ralph-tester subagents via Task tool

This skill coordinates these tools while keeping you in control at decision points.

<essential_principles>

ALL code implementation MUST happen through subagents — ralph-coder implements production code, ralph-tester writes tests and verifies. You are the orchestrator, NOT the implementer. Do not write code, create files, modify source files, or make any project changes directly.

If you catch yourself about to write code or modify project files: STOP. Spawn a subagent instead.

Do NOT try to fix issues yourself, retry automatically, or continue past errors. Present the error clearly to the user and wait for their instructions.

This maximizes throughput while maintaining correct dependency ordering.

Separation gives each agent a focused context window. The orchestrator wraps ANY agent with Ralph context (story spec, return format, constraints) so even non-Ralph agents integrate seamlessly.

This lets users configure best-practice agents for their stack, and Ralph automatically uses them.

Both agents also update the docs/ folder with documentation about new features, APIs, test setup, and architecture changes. The tasks/test-log.md and tasks/review-notes.md files are updated by tester agents with test registries and improvement recommendations.

Never assume agents "remember" previous stories — but they CAN read shared knowledge files.

Right-sized:

  • Add a database column
  • Create a UI component
  • Update a server action
  • Implement a filter

Too large (will fail):

  • Build entire dashboard
  • Add authentication system
  • Refactor entire API
  • API stories: curl endpoints with real data, check response codes and bodies
  • UI stories: Playwright e2e tests that navigate and interact with real UI
  • Database stories: Run migrations, query DB directly to confirm schema
  • Infra stories: Health checks, config validation, service startup

Static checks (typecheck, lint) are baseline. Runtime validation is required.

After each story, the tester runs ALL existing tests (via testCommands in prd.json root) to catch regressions. A story is NOT done until the entire test suite passes.

Stories track attempts / maxAttempts to prevent infinite retries on broken stories.

Don't rush. Bad requirements = wasted iterations.

</essential_principles>

<prd_json_schema>

{
  "project": "[Project Name]",
  "branchName": "ralph/[feature-name-kebab-case]",
  "description": "[Feature description]",
  "testCommands": {
    "unit": "npm test",
    "integration": "npm run test:integration",
    "e2e": "npx playwright test",
    "typecheck": "npm run typecheck"
  },
  "userStories": [
    {
      "id": "US-001",
      "title": "[Story title]",
      "description": "As a [user], I want [feature] so that [benefit]",
      "storyType": "backend | frontend | database | api | infra | test",
      "acceptanceCriteria": ["Specific criterion 1", "Typecheck passes"],
      "verificationCommands": [
        { "command": "npm run typecheck", "expect": "exit_code:0" },
        { "command": "curl -s http://localhost:3000/api/...", "expect": "contains:expected" }
      ],
      "status": "pending",
      "priority": 1,
      "attempts": 0,
      "maxAttempts": 3,
      "notes": "",
      "blockedBy": [],
      "docsToUpdate": ["README.md", "docs/api.md"],
      "completedAt": null,
      "lastAttemptLog": ""
    }
  ]
}

Expect matchers for verificationCommands:

  • exit_code:0 — command exits with code 0
  • exit_code:N — command exits with specific code N
  • contains:STRING — stdout contains STRING
  • not_empty — stdout is non-empty
  • matches:REGEX — stdout matches regex pattern </prd_json_schema>
  1. Full pipeline - Start from scratch (spec → PRD → prd.json → execute)
  2. Continue from PRD - Already have PRD, convert and execute
  3. Execute only - Already have prd.json, run Ralph
  4. Check status - View current prd.json progress

Wait for response before proceeding.

After reading the workflow, follow it exactly.

<quick_reference>

Key Files:

FilePurpose
SPEC.mdComprehensive requirements from spec-interview
tasks/prd-*.mdProduct Requirements Document
tasks/prd.jsonAtomic user stories for Ralph
tasks/progress.txtLearnings between iterations
tasks/test-log.mdRegistry of all tests created per story (updated by tester agents)
tasks/review-notes.mdImprovement recommendations after each story (updated by tester agents)
tasks/common_knowledge.mdShared knowledge base — patterns, conventions, gotchas discovered across stories (updated by both coder and tester agents, read by orchestrator between batches)
docs/Project documentation — updated by both coder and tester agents with new features, APIs, test setup, etc.

Agents:

AgentRoleFallback
ralph-coderImplements production code + docs for one storyDefault coder when no project-specific agent matches
ralph-testerWrites tests + runs verification for one storyDefault tester when no project-specific agent matches
Project agentsDiscovered from.claude/agents/ and ~/.claude/agents/Matched to storyTypes by description keywords

Execution model:

BATCH 1 (independent stories):
  Phase 1: Task(coder, US-001, worktree) + Task(coder, US-005, worktree)  ← parallel
  Phase 2: Task(tester, US-001) + Task(tester, US-005)                    ← parallel
  Merge: US-001 → main, US-005 → main                                    ← sequential

BATCH 2 (stories that depended on BATCH 1):
  Phase 1: Task(coder, US-002, worktree) + Task(coder, US-003, worktree)
  Phase 2: Task(tester, US-002) + Task(tester, US-003)
  Merge: US-002 → main, US-003 → main

Commands:

# Check story status
cat tasks/prd.json | jq '.userStories[] | {id, title, status, attempts}'

# View learnings
cat tasks/progress.txt

# View test registry
cat tasks/test-log.md

# View review notes
cat tasks/review-notes.md

</quick_reference>

<workflows_index>

WorkflowPurpose
full-pipeline.mdComplete flow: spec → PRD → prd.json → execute
from-prd.mdConvert existing PRD and execute
execute-only.mdRun Ralph on existing prd.json
check-status.mdView current progress
</workflows_index>

<success_criteria> Pipeline is complete when:

  • Requirements gathered through spec-interview (including verification environment)
  • PRD created with verifiable acceptance criteria
  • prd.json has atomic stories with storyType, verificationCommands, and blockedBy
  • All stories have status: "done" in prd.json
  • All verification commands passed (real runtime checks, not just typecheck)
  • Code committed and merged to main via worktree branches </success_criteria>

平台分布

Claude Code

29.49%
按下载量换算37

windsurf

24.45%
按下载量换算30

OpenCode

16.87%
按下载量换算21

Codex

13.62%
按下载量换算17

Antigravity

7.47%
按下载量换算9

Gemini CLI

3.58%
按下载量换算4

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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