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ralphRalph 助手

Agent Skill

ralph 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

1,848

周安装

77

GitHub Stars

26,774

下载量

616
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

2

许可证

unknown

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(Ralph 助手)
来源仓库:https://github.com/yeachan-heo/oh-my-codex
仓库路径:skills/ralph
安装命令:
npx skills add https://github.com/yeachan-heo/oh-my-codex --skill ralph
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/yeachan-heo/oh-my-codex --skill ralph

简介

ralph 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定仓库安装并使用该技能。
  • 安装前需确认权限范围和维护状态,注意可能触发联网、命令执行或文件读写操作。
  • ralph 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

[RALPH + ULTRAWORK - ITERATION {{ITERATION}}/{{MAX}}]

Your previous attempt did not output the completion promise. Continue working on the task.

<Use_When>

  • Task requires guaranteed completion with verification (not just "do your best")
  • User says "ralph", "don't stop", "must complete", "finish this", or "keep going until done"
  • Work may span multiple iterations and needs persistence across retries
  • Task benefits from parallel execution with architect sign-off at the end </Use_When>

<Do_Not_Use_When>

  • User wants a full autonomous pipeline from idea to code -- use autopilot instead
  • User wants to explore or plan before committing -- use plan skill instead
  • User wants a quick one-shot fix -- delegate directly to an executor agent
  • User wants manual control over completion -- use ultrawork directly </Do_Not_Use_When>

<Why_This_Exists> Complex tasks often fail silently: partial implementations get declared "done", tests get skipped, edge cases get forgotten. Ralph prevents this by looping until work is genuinely complete, requiring fresh verification evidence before allowing completion, and using tiered architect review to confirm quality. </Why_This_Exists>

<Execution_Policy>

  • Fire independent agent calls simultaneously -- never wait sequentially for independent work
  • Use run_in_background: true for long operations (installs, builds, test suites)
  • Always pass the model parameter explicitly when delegating to agents
  • Read docs/shared/agent-tiers.md before first delegation to select correct agent tiers
  • Deliver the full implementation: no scope reduction, no partial completion, no deleting tests to make them pass
  • Apply the shared workflow guidance pattern: concise, evidence-dense progress and completion reporting, local overrides for the active workflow branch, persistent inspection/verification while the execution loop depends on it, and automatic continuation for safe reversible steps. Ask only for material, destructive, or preference-dependent branches. </Execution_Policy>

<Tool_Usage>

  • Before first MCP tool use, call ToolSearch("mcp") to discover deferred MCP tools
  • Use ask_codex with agent_role: "architect" for verification cross-checks when changes are security-sensitive, architectural, or involve complex multi-system integration
  • Skip Codex consultation for simple feature additions, well-tested changes, or time-critical verification
  • If ToolSearch finds no MCP tools or Codex is unavailable, proceed with architect agent verification alone -- never block on external tools
  • Use state_write / state_read for ralph mode state persistence between iterations
  • Persist context snapshot path in Ralph mode state so later phases and agents share the same grounding context
  • If an omx_state MCP tool call reports that its stdio transport is unavailable/closed, do not retry the same MCP call. Retry once through the supported CLI parity surface with the same payload, preserving workingDirectory and session_id: omx state write --input '<json>' --json, omx state read --input '<json>' --json, or omx state clear --input '<json>' --json. If the CLI path also fails, continue with .omx/context / .omx/plans file-backed artifacts and report the state persistence blocker. </Tool_Usage>

State Management

Use the omx_state MCP server tools (state_write, state_read, state_clear) for Ralph lifecycle state.

  • On start: state_write({mode: "ralph", active: true, iteration: 1, max_iterations: 10, current_phase: "executing", started_at: "<now>", state: {context_snapshot_path: "<snapshot-path>"}})
  • On each iteration: state_write({mode: "ralph", iteration: <current>, current_phase: "executing"})
  • On verification/fix transition: state_write({mode: "ralph", current_phase: "verifying"}) or state_write({mode: "ralph", current_phase: "fixing"})
  • On completion: state_write({mode: "ralph", active: false, current_phase: "complete", completed_at: "<now>"})
  • On cancellation/cleanup: run $cancel (which should call state_clear(mode="ralph"))

Scenario Examples

Good: The user says continue after the workflow already has a clear next step. Continue the current branch of work instead of restarting or re-asking the same question.

Good: The user changes only the output shape or downstream delivery step (for example make a PR). Preserve earlier non-conflicting workflow constraints and apply the update locally.

Bad: The user says continue, and the workflow restarts discovery or stops before the missing verification/evidence is gathered.

<Escalation_And_Stop_Conditions>

  • Stop and report when a fundamental blocker requires user input (missing credentials, unclear requirements, external service down)
  • Stop when the user says "stop", "cancel", or "abort" -- run /cancel
  • Continue working when the hook system sends "The boulder never stops" -- this means the iteration continues
  • If architect rejects verification, fix the issues and re-verify (do not stop)
  • If the same issue recurs across 3+ iterations, report it as a potential fundamental problem </Escalation_And_Stop_Conditions>

<Final_Checklist>

  • All requirements from the original task are met (no scope reduction)
  • Zero pending or in_progress TODO items
  • Fresh test run output shows all tests pass
  • Fresh build output shows success
  • lsp_diagnostics shows 0 errors on affected files
  • Architect verification passed (STANDARD tier minimum)
  • ai-slop-cleaner pass completed on changed files (or --no-deslop specified)
  • Post-deslop regression tests pass
  • /cancel run for clean state cleanup </Final_Checklist>

When the user provides the --prd flag, initialize a Product Requirements Document before starting the ralph loop.

Detecting PRD Mode

Check if {{PROMPT}} contains --prd or --PRD.

Prompt-side $ralph workflow activation is lighter-weight than omx ralph --prd.... It seeds Ralph workflow state and guidance, but it does not implicitly launch the CLI entrypoint or apply the PRD startup gate. Treat omx ralph --prd... as the explicit PRD-gated path.

Detecting --no-deslop

Check if {{PROMPT}} contains --no-deslop. If --no-deslop is present, skip the deslop pass entirely after Step 7 and continue using the latest successful pre-deslop verification evidence.

Visual Reference Flags (Optional)

Ralph execution supports visual reference flags for screenshot tasks:

  • Repeatable image inputs: -i <image-path> (can be used multiple times)
  • Image directory input: --images-dir <directory>

Example: ralph -i refs/hn.png -i refs/hn-item.png --images-dir./screenshots "match HackerNews layout"

PRD Workflow

  1. Run deep-interview in quick mode before creating PRD artifacts:

- Execute: $deep-interview --quick <task> - Complete a compact requirements pass (context, goals, scope, constraints, validation) - Persist interview output to .omx/interviews/{slug}-{timestamp}.md

  1. Create canonical PRD/progress artifacts:

- PRD: .omx/plans/prd-{slug}.md - Progress ledger: .omx/state/{scope}/ralph-progress.json (session scope when available, else root scope)

  1. Parse the task (everything after --prd flag)
  2. Break down into user stories:
{
  "project": "[Project Name]",
  "branchName": "ralph/[feature-name]",
  "description": "[Feature description]",
  "userStories": [
    {
      "id": "US-001",
      "title": "[Short title]",
      "description": "As a [user], I want to [action] so that [benefit].",
      "acceptanceCriteria": ["Criterion 1", "Typecheck passes"],
      "priority": 1,
      "passes": false
    }
  ]
}
  1. Initialize canonical progress ledger at .omx/state/{scope}/ralph-progress.json
  2. Guidelines: right-sized stories (one session each), verifiable criteria, independent stories, priority order (foundational work first)
  3. Proceed to normal ralph loop using user stories as the task list

Example

User input: --prd build a todo app with React and TypeScript Workflow: Detect flag, extract task, create .omx/plans/prd-{slug}.md, create .omx/state/{scope}/ralph-progress.json, begin ralph loop.

Legacy compatibility

  • During the compatibility window, Ralph --prd startup still validates machine-readable story state from .omx/prd.json.
  • .omx/plans/prd-{slug}.md remains the canonical storage/documentation artifact, but it is not yet the startup validation source.
  • If .omx/prd.json exists and canonical PRD is absent, migrate one-way into .omx/plans/prd-{slug}.md.
  • If .omx/progress.txt exists and canonical progress ledger is absent, import one-way into .omx/state/{scope}/ralph-progress.json.
  • Keep legacy files unchanged for one release cycle.

Background Execution Rules

Run in background (run_in_background: true):

  • Package installation (npm install, pip install, cargo build)
  • Build processes (make, project build commands)
  • Test suites
  • Docker operations (docker build, docker pull)

Run blocking (foreground):

  • Quick status checks (git status, ls, pwd)
  • File reads and edits
  • Simple commands

Original task: {{PROMPT}}

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

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

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

Codex

34.1%
按下载量换算210

Claude

31.14%
按下载量换算192

Cursor

18.86%
按下载量换算116

Gemini CLI

10.35%
按下载量换算64

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

external-service

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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