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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

8,811

周安装

353

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32,015

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2,852
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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(Ralph 助手)
来源仓库:https://github.com/yeachan-heo/oh-my-claudecode
仓库路径:skills/ralph
安装命令:
npx skills add https://github.com/yeachan-heo/oh-my-claudecode --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。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/yeachan-heo/oh-my-claudecode --skill ralph

简介

用于查找、检索和筛选相关信息。ralph 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合根据关键词或任务场景快速定位候选结果。
  • 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。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 注意是否会触发联网、命令执行或文件读写。

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 structured PRD-driven execution with reviewer sign-off </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:

  1. Structuring work into discrete user stories with testable acceptance criteria (prd.json)
  2. Iterating story-by-story until each one passes
  3. Tracking progress and learnings across iterations (progress.txt)
  4. Requiring fresh reviewer verification against specific acceptance criteria before completion </Why_This_Exists>

<PRD_Mode> By default, ralph operates in PRD mode. A scaffold prd.json is auto-generated when ralph starts if none exists. Active transient PRD state is session-scoped at .omc/state/sessions/{sessionId}/prd.json when a session ID is available; legacy project-level prd.json / .omc/prd.json files are read as startup migration inputs.

Startup gate: Ralph always initializes and validates prd.json at startup. Legacy --no-prd text is sanitized from the prompt for backward compatibility, but it no longer bypasses PRD creation or validation.

Deslop opt-out: If {{PROMPT}} contains --no-deslop, skip the mandatory post-review deslop pass entirely. Use this only when the cleanup pass is intentionally out of scope for the run.

Reviewer selection: Pass --critic=architect, --critic=critic, or --critic=codex in the Ralph prompt to choose the completion reviewer for that run. architect remains the default. </PRD_Mode>

<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 </Execution_Policy>
  1. Pick next story: Read the active PRD file and select the highest-priority story with passes: false. This is your current focus.
  2. Implement the current story:

- Delegate to specialist agents at appropriate tiers: - Simple lookups: LOW tier (Haiku) -- "What does this function return?" - Standard work: MEDIUM tier (Sonnet) -- "Add error handling to this module" - Complex analysis: HIGH tier (Opus) -- "Debug this race condition" - If during implementation you discover sub-tasks, add them as new stories to the active PRD file - Run long operations in background: Builds, installs, test suites use run_in_background: true

  1. Verify the current story's acceptance criteria: a. For EACH acceptance criterion in the story, verify it is met with fresh evidence b. Run relevant checks (test, build, lint, typecheck) and read the output c. If any criterion is NOT met, continue working -- do NOT mark the story as complete
  2. Mark story complete: a. When ALL acceptance criteria are verified, set passes: true for this story in the active PRD file b. Record progress in progress.txt: what was implemented, files changed, learnings for future iterations c. Add any discovered codebase patterns to progress.txt
  3. Check PRD completion: a. Read the active PRD file -- are ALL stories marked passes: true? b. If NOT all complete, loop back to Step 2 (pick next story) c. If ALL complete, proceed to Step 7 (architect verification)
  4. Reviewer verification (tiered, against acceptance criteria):

- <5 files, <100 lines with full tests: STANDARD tier minimum (architect-medium / Sonnet) - Standard changes: STANDARD tier (architect-medium / Sonnet) - 20 files or security/architectural changes: THOROUGH tier (architect / Opus) - If --critic=critic, use the Claude critic agent for the approval pass - If --critic=codex, run omc ask codex --agent-prompt critic "..." for the approval pass. The Codex critic prompt MUST include: 1. The full list of acceptance criteria from prd.json for verification 2. A directive to evaluate whether the implementation is OPTIMAL — not just correct, but whether there exists a meaningfully better approach (simpler, faster, more maintainable) that the implementation missed 3. A directive to review all code related to the changes (callers, callees, shared types, adjacent modules), not only the files directly modified 4. The list of files changed during the ralph session for context - Ralph floor: always at least STANDARD, even for small changes - The selected reviewer verifies against the SPECIFIC acceptance criteria from prd.json, not vague "is it done?" - On APPROVAL: immediately proceed to Step 7.5 in the same turn. Do NOT pause to report the verdict to the user — reporting happens only at Step 8 (/oh-my-claudecode:cancel) or on rejection (Step 9). Treating an approved verdict as a reporting checkpoint is a polite-stop anti-pattern.

7.5 Mandatory Deslop Pass (runs unconditionally after Step 7 approval, unless {{PROMPT}} contains --no-deslop):

  • Invoke the ai-slop-cleaner skill via the Skill tool: Skill("ai-slop-cleaner"). Run in standard mode (not --review) on the files changed during the current Ralph session only.
  • ai-slop-cleaner is a SKILL, not an agent. Do NOT call it via Task(subagent_type="oh-my-claudecode:ai-slop-cleaner") — that subagent type does not exist and the call will fail with "Agent type not found". If you see that error, retry with the Skill tool — do NOT substitute a similarly-named agent like code-simplifier as a "closest match".
  • Keep the scope bounded to the Ralph changed-file set; do not broaden the cleanup pass to unrelated files.
  • If the reviewer approved the implementation but the deslop pass introduces follow-up edits, keep those edits inside the same changed-file scope before proceeding.

7.6 Regression Re-verification:

  • After the deslop pass, re-run all relevant tests, build, and lint checks for the Ralph session.
  • Read the output and confirm the post-deslop regression run actually passes.
  • If regression fails, roll back the cleaner changes or fix the regression, then rerun the verification loop until it passes.
  • Only proceed to completion after the post-deslop regression run passes (or --no-deslop was explicitly specified).
  1. On approval: After Step 7.6 passes (with Step 7.5 completed, or skipped via --no-deslop), run /oh-my-claudecode:cancel to cleanly exit and clean up all state files
  2. On rejection: Fix the issues raised, re-verify with the same reviewer, then loop back to check if the story needs to be marked incomplete

<Tool_Usage>

  • Use Task(subagent_type="oh-my-claudecode:architect",...) for architect verification cross-checks when changes are security-sensitive, architectural, or involve complex multi-system integration
  • Use Task(subagent_type="oh-my-claudecode:critic",...) when --critic=critic
  • Use omc ask codex --agent-prompt critic "..." when --critic=codex. Construct the prompt to include: (a) prd.json acceptance criteria, (b) files changed + related files, (c) explicit optimality question: "Is there a meaningfully simpler, faster, or more maintainable approach that achieves the same acceptance criteria?"
  • Skip architect consultation for simple feature additions, well-tested changes, or time-critical verification
  • Proceed with architect agent verification alone -- never block on unavailable tools
  • Use state_write / state_read for ralph mode state persistence between iterations
  • Skill vs agent invocation: ai-slop-cleaner is a skill, invoke via Skill("ai-slop-cleaner"). architect, critic, executor etc. are agents, invoke via Task(subagent_type="oh-my-claudecode:<name>"). If you ever get "Agent type... not found" for an oh-my-claudecode:<name> identifier, the item is a skill — retry with the Skill tool. Do NOT substitute a similarly-named agent as a "closest match". </Tool_Usage>

After refinement: acceptanceCriteria: ["Legacy --no-prd text is stripped from the Ralph working prompt", "Ralph startup still creates or validates prd.json when legacy --no-prd text is present", "TypeScript compiles with no errors (npm run build)"]

Why good: Generic criteria replaced with specific, testable criteria.
</Good>

<Good>
Correct parallel delegation:

Task(subagent_type="oh-my-claudecode:executor", model="haiku", prompt="Add type export for UserConfig") Task(subagent_type="oh-my-claudecode:executor", model="sonnet", prompt="Implement the caching layer for API responses") Task(subagent_type="oh-my-claudecode:executor", model="opus", prompt="Refactor auth module to support OAuth2 flow")

Why good: Three independent tasks fired simultaneously at appropriate tiers.
</Good>

<Good>
Story-by-story verification:
  1. Story US-001: "Add flag detection helpers"

- Criterion: "Legacy --no-prd is stripped from the working prompt" → Run test → PASS - Criterion: "TypeScript compiles" → Run build → PASS - Mark US-001 passes: true

  1. Story US-002: "Wire PRD into bridge.ts"

- Continue to next story...

Why good: Each story verified against its own acceptance criteria before marking complete.
</Good>

<Bad>
Claiming completion without PRD verification:
"All the changes look good, the implementation should work correctly. Task complete."
Why bad: Uses "should" and "look good" -- no fresh evidence, no story-by-story verification, no architect review.
</Bad>

<Bad>
Sequential execution of independent tasks:

Task(executor, "Add type export") → wait → Task(executor, "Implement caching") → wait → Task(executor, "Refactor auth")

Why bad: These are independent tasks that should run in parallel, not sequentially.
</Bad>

<Bad>
Keeping generic acceptance criteria:
"prd.json created with criteria: Implementation is complete, Code compiles. Moving on to coding."
Why bad: Did not refine scaffold criteria into task-specific ones. This is PRD theater.
</Bad>
</Examples>

<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 `/oh-my-claudecode:cancel`
- Continue working when the hook system sends "The boulder never stops" -- this means the iteration continues
- If the selected reviewer 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
- **Do NOT stop after Step 7 approval.** The boulder continues through 7 → 7.5 → 7.6 → 8 in the same turn as a single chain. Step 7 is a checkpoint inside the loop, not a reporting moment. Treating an architect/critic APPROVED verdict as "time to summarise and wait for user acknowledgment" is a polite-stop anti-pattern — the only reporting moments in Ralph are Step 8 (successful cancel) or Step 9 (rejection).
</Escalation_And_Stop_Conditions>

<Final_Checklist>
- [ ] All prd.json stories have `passes: true` (no incomplete stories)
- [ ] prd.json acceptance criteria are task-specific (not generic boilerplate)
- [ ] 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
- [ ] progress.txt records implementation details and learnings
- [ ] Selected reviewer verification passed against specific acceptance criteria
- [ ] ai-slop-cleaner pass completed on changed files (or `--no-deslop` specified)
- [ ] Post-deslop regression tests pass
- [ ] `/oh-my-claudecode:cancel` run for clean state cleanup
</Final_Checklist>

<Advanced>
## 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
</Advanced>

Original task:
{{PROMPT}}

适合场景

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用户想查找某类 Agent Skill 时

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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

28.73%
按下载量换算819

OpenCode

21.79%
按下载量换算621

Gemini CLI

16.19%
按下载量换算462

Antigravity

11.58%
按下载量换算330

Cursor

8.26%
按下载量换算236

Codex

3.72%
按下载量换算106

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

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