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ralph-orchestrator拉尔夫·协调器

Agent Skill

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。

总安装

353

周安装

15

GitHub Stars

16

下载量

124
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-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

简介

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。

SKILL.md

  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>

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

29.49%
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 warning/failed 结果,不能写成本站确认安全。

来源信息

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