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Agent Skill · 研究检索

ultrawork(ultrawork 搜索)

ultrawork 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。

ultrawork 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,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 40,719。

总安装 1,259周安装 53GitHub Stars 26,798下载 441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:ultrawork(ultrawork 搜索)
来源仓库:https://github.com/yeachan-heo/oh-my-codex
仓库路径:skills/ultrawork
安装命令:
npx skills add https://github.com/yeachan-heo/oh-my-codex --skill ultrawork
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 ultrawork

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。

ultrawork 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。

ultrawork 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/ultrawork

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/yeachan-heo/oh-my-codex --skill ultrawork;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

ultrawork 怎么安装?

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

ultrawork 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。

ultrawork 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。

ultrawork 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。

ultrawork 用的人多吗?

公开数据是累计安装 1,259 次,近一周安装 53 次,下载 441 次,GitHub 26,798 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。

ultrawork 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 26,798 星,维护活跃度相对可查。

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。

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
ultrawork

ultrawork 搜索 · 本页

1,25926,798调用外部服务未明确通过

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

<Use_When>

  • Multiple independent tasks can run simultaneously
  • User says "ulw", "ultrawork", or explicitly wants parallel execution
  • Task benefits from concurrent execution plus lightweight evidence before wrap-up
  • You need a direct-tool lane plus optional background evidence lanes without entering Ralph </Use_When>

<Do_Not_Use_When>

  • Task requires guaranteed completion with persistence, architect verification, or deslop/reverification -- use ralph instead (Ralph includes ultrawork)
  • Task requires a full autonomous pipeline -- use autopilot instead (autopilot includes Ralph which includes ultrawork)
  • There is only one sequential task with no parallelism opportunity -- execute directly or delegate to a single executor
  • The request is still in plan-consensus mode -- keep planning artifacts in ralplan until execution is explicitly authorized
  • User needs session persistence for resume -- use ralph, which adds persistence on top of ultrawork </Do_Not_Use_When>

<Why_This_Exists> Sequential task execution wastes time when tasks are independent. Ultrawork keeps the execution branch fast while tightening the protocol: gather enough context first, define pass/fail acceptance criteria before editing, decide deliberately between local execution and delegation, and finish with evidence rather than vibes. </Why_This_Exists>

<Execution_Policy>

  • Gather enough context before implementation. Start with the task intent, desired outcome, constraints, likely touchpoints, and any uncertainty that would change the execution path.
  • If uncertainty is still material after a quick repo read, do a focused evidence pass first instead of immediately editing.
  • Define pass/fail acceptance criteria before launching execution lanes. Include the command, artifact, or manual check that will prove success.
  • Prefer direct tool work when the task is small, coupled, or blocked on immediate local context. Delegate only when the work is independent enough to benefit from parallel execution.
  • When useful, run a direct-tool lane and one or more background evidence lanes at the same time. Evidence lanes can cover docs, tests, regression mapping, or bounded repo analysis.
  • Fire independent agent calls simultaneously -- never serialize independent work.
  • Always pass the model parameter explicitly when delegating.
  • Read docs/shared/agent-tiers.md before first delegation for agent selection guidance.
  • Auto-delegate researcher when official docs, version-aware framework guidance, best practices, or external dependency behavior materially affect task correctness; treat it as an evidence lane, not a replacement primary workflow.
  • Use run_in_background: true for operations over ~30 seconds (installs, builds, tests).
  • Run quick commands (git status, file reads, simple checks) in the foreground.
  • Apply the shared workflow guidance pattern: concise, evidence-dense progress and completion reporting (including speculative/blocked lanes), local overrides for the active workflow branch, and continuation of clear safe execution branches instead of restarting or re-asking.
  • If the user says continue, continue the active workflow branch rather than restarting discovery or re-asking settled questions. </Execution_Policy>

<Tool_Usage>

  • Use LOW-tier delegation for simple lookups and bounded evidence gathering.
  • Use STANDARD-tier delegation for standard implementation and regression work.
  • Use THOROUGH-tier delegation for complex analysis, architectural review, or risky multi-file changes.
  • Prefer a direct-tool lane when the immediate next step is blocked on local context.
  • Prefer background evidence lanes when you can learn something useful in parallel with implementation.
  • Use run_in_background: true for package installs, builds, and test suites.
  • Use foreground execution for quick status checks and file operations. </Tool_Usage>

State Management

Use omx_state MCP tools for ultrawork lifecycle state.

  • On start: state_write({mode: "ultrawork", active: true, reinforcement_count: 1, started_at: "<now>"})
  • On each reinforcement/loop step: state_write({mode: "ultrawork", reinforcement_count: <current>})
  • On completion: state_write({mode: "ultrawork", active: false})
  • On cancellation/cleanup: run $cancel (which should call state_clear(mode="ultrawork"))

Direct-tool lane:

  • update skills/ultrawork/SKILL.md

Background evidence lane:

  • delegate(role="test-engineer", tier="STANDARD", task="Map which hook tests cover ultrawork activation messaging", model="...")
Why good: Context is grounded first, acceptance criteria are explicit, and the direct-tool lane runs alongside a bounded evidence lane.
</Good>

<Good>
Correct use of self-vs-delegate judgment:

Shared-file edit in progress across src/scripts/codex-native-hook.ts and its test -> keep implementation local. Independent regression mapping for keyword-detector coverage -> delegate to a test-engineer lane.

Why good: Shared-file work stays local; independent evidence work fans out.
</Good>

<Bad>
Parallelizing before the task is grounded:

delegate(role="executor", tier="STANDARD", task="Implement whatever seems necessary", model="...") delegate(role="test-engineer", tier="STANDARD", task="Figure out how to test it later", model="...")

Why bad: No context snapshot, no pass/fail target, and delegation starts before the work is shaped.
</Bad>

<Bad>
Claiming success without evidence or manual QA:

Made the changes. Ultrawork should be updated now.

Why bad: No verification output, no acceptance evidence, and no manual QA note when the behavior is user-visible.
</Bad>
</Examples>

<Escalation_And_Stop_Conditions>
- When ultrawork is invoked directly (not via Ralph), apply lightweight verification only -- build/typecheck passes when relevant, affected tests pass, and manual QA notes are captured when needed.
- Ralph owns persistence, architect verification, deslop, and the full verified-completion promise. Do not claim those guarantees from direct ultrawork alone.
- If a task fails repeatedly across retries, report the issue rather than retrying indefinitely.
- Escalate to the user when tasks have unclear dependencies, conflicting requirements, or a materially branching acceptance target.
</Escalation_And_Stop_Conditions>

<Final_Checklist>
- [ ] Task intent and constraints were grounded before editing
- [ ] Pass/fail acceptance criteria were stated before execution
- [ ] Parallel lanes were used only for independent work
- [ ] Build/typecheck passes when relevant
- [ ] Affected tests pass
- [ ] Manual QA notes recorded when behavior is user-visible
- [ ] No new errors introduced
- [ ] Completion claim stays inside ultrawork's lightweight-verification boundary
</Final_Checklist>

<Advanced>
## Relationship to Other Modes

ralph (persistence + verified completion wrapper) -- includes: ultrawork (this skill) -- provides: high-throughput execution + lightweight evidence

autopilot (autonomous execution) -- includes: ralph -- includes: ultrawork (this skill)

ecomode (token efficiency) -- modifies: ultrawork's model selection

Ultrawork is the parallelism and execution-discipline layer. Ralph adds persistence, architect verification, deslop, and retry-until-done behavior. Autopilot adds the broader autonomous lifecycle pipeline. Ecomode adjusts ultrawork's model routing to favor cheaper models.
</Advanced>

平台分布

Codex

32.85%
按下载量换算145

Claude

32.31%
按下载量换算142

Cursor

21.04%
按下载量换算93

Gemini CLI

8.87%
按下载量换算39

安全审计

Gen Agent Trust Hub

通过

Socket

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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