智能体

SKILLAI 工具
AI 工具 未标注权限 许可证不明确
安装这个技能

开放式主题研究,构建活的Markdown文档,支持交互(付费增强版)。Conduct open-ended research on a topic,

一句话选型结论

按资料字段整理

智能体是AI 工具类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类AI 工具 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install zcwl/research-agent
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

AI 工具
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方zcwl仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 enterprise
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791618380
正文体量6.9 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
组合多个 AI 能力

把写作、检索、总结串成一条流程。

搭工作流

把固定流程做成可重复调用的步骤。

做提示词与评测

整理提示词并对比输出效果。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在AI 工具场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
智能体当前 未标注权限 未标注 未标注 未标注 有
执行计划 只读 GitHub 仓库 151.5 万 Codex、Claude、Cursor 有
Playwright MCP 测试 浏览器控制 ClawHub 市场 136.5 万 OpenClaw 有
完成前的验证 未标注权限 GitHub 仓库 131.9 万 Codex、Claude、Cursor 有
子 Agent 驱动开发 只读 GitHub 仓库 131.2 万 Codex、Claude、Cursor 有
前端设计 执行命令 GitHub 仓库 125.6 万 Codex、Claude、Cursor 有

SKILL.md 全文

6.9 KB
模型实际读到的说明书原文,来自技能仓库

Research Skill

Description

Conduct open-ended research on a topic, building a living markdown document. The conversation is ephemeral; the document is what matters.

Trigger

Activate when the user wants to:
- Research a topic, idea, or question
- Explore something before committing to building it
- Investigate options, patterns, or approaches
- Create a "research doc" or "investigation"
- Run deep async research on a complex topic

Research Directory

Each research topic gets its own folder:

~/.openclaw/workspace/research/<topic-slug>/
├── prompt.md          # Original research question/prompt
├── research.md        # Main findings (Parallel output or interactive notes)
├── research.pdf       # PDF export (when generated)
└── ...                # Any other related files (data, images, etc.)

Two Research Modes

1. Interactive Research (default)

For topics you explore together in conversation. You search, synthesize, and update the doc in real-time.

2. Deep Research (async)

For complex topics that need comprehensive investigation. Uses the Parallel AI API via parallel-research CLI. Takes minutes to hours, returns detailed markdown reports.

When to use deep research:
- Market analysis, competitive landscape
- Technical deep-dives requiring extensive source gathering
- Multi-faceted questions that benefit from parallel exploration
- When user says "deep research" or wants comprehensive coverage


Interactive Research Workflow

1. Initialize Research

  1. Create the research folder at ~/.openclaw/workspace/research/<topic-slug>/

  2. Create prompt.md with the original question:
    ```markdown
    # <Topic Title>

> <The core question or curiosity>

Started: <date>
```

  1. Create research.md with the working structure:
    ```markdown
    # <Topic Title>

Status: Active Research
Started: <date>
Last Updated: <date>


## Open Questions
- <initial questions to explore>

## Findings
<!-- Populated as we research -->

## Options / Approaches
<!-- If comparing solutions -->

## Resources
<!-- Links, references, sources -->

## Next Steps
<!-- What to explore next, or "graduate to project" -->
```

  1. Confirm with user - Show the folder was created and ask what to explore first.

2. Research Loop

For each exchange:

  1. Do the research - Web search, fetch docs, explore code
  2. Update the document - Add findings, move answered questions, add sources
  3. Show progress - Note what was added (don't repeat everything)
  4. Prompt next direction - End with a question or suggestion

Key behaviors:
- Update existing sections over creating new ones
- Use bullet points for findings; prose for summaries
- Note uncertainty ("seems like", "according to X", "unverified")
- Link to sources whenever possible

3. Synthesis Checkpoints

Every 5-10 exchanges, offer to:
- Write a "Current Understanding" summary
- Prune redundant findings
- Reorganize if unwieldy
- Check blind spots

4. Completion

When research is complete, update the status in research.md:

  • "Status: Complete" — Done, stays in place as reference
  • "Status: Ongoing" — Living doc, will be updated over time

If the research is specifically for building a project:
- Graduate to ~/specs/&lt;project-name&gt;.md as a project spec
- Or create a project directly based on findings
- Update status to "Status: Graduated → ~/specs/..."

Most research is just research — it doesn't need to become a spec. Only graduate if you're actually building something from it.


Deep Research Workflow

1. Start Deep Research

parallel-research create &quot;Your research question&quot; --processor ultra --wait

Processor options:
- lite, base, core, pro, ultra (default), ultra2x, ultra4x, ultra8x
- Add -fast suffix for speed over depth: ultra-fast, pro-fast, etc.

Options:
- -w, --wait — Wait for completion and show result
- -p, --processor — Choose processor tier
- -j, --json — Raw JSON output

2. Schedule Auto-Check (optional)

Deep research tasks take minutes to hours. You'll want to poll for results automatically rather than checking manually.

Options:
- OpenClaw users: See OPENCLAW.md for cron-based auto-check scheduling
- Other setups: Use any scheduler (cron, systemd timer, CI job) to periodically run parallel-research status &lt;run_id&gt; and parallel-research result &lt;run_id&gt; until complete
- Simple approach: Just use parallel-research create &quot;...&quot; --wait to block until done (works for shorter tasks)

3. Manual Check (if needed)

parallel-research status &lt;run_id&gt;
parallel-research result &lt;run_id&gt;

4. Save to Research Folder

Create the research folder and save results:

~/.openclaw/workspace/research/&lt;topic-slug&gt;/
├── prompt.md          # Original question + run metadata
├── research.md        # Full Parallel output

prompt.md should include:

# &lt;Topic Title&gt;

&gt; &lt;Original research question&gt;

**Run ID:** &lt;run_id&gt;
**Processor:** &lt;processor&gt;
**Started:** &lt;date&gt;
**Completed:** &lt;date&gt;

research.md contains the full Parallel output, plus any follow-up notes.


PDF Export

All PDFs go in the research folder — never save to tmp/. Whether using export-pdf, the browser pdf action, or any other method, the output path must be research/&lt;topic-slug&gt;/.

Use the export-pdf script to convert research docs to PDF:

export-pdf ~/.openclaw/workspace/research/&lt;topic-slug&gt;/research.md
# Creates: ~/.openclaw/workspace/research/&lt;topic-slug&gt;/research.pdf

For browser-generated PDFs (e.g. saving a webpage as PDF):

browser pdf → save to research/&lt;topic-slug&gt;/&lt;descriptive-name&gt;.pdf

Note: Tables render as stacked rows (PyMuPDF limitation). Acceptable for research docs.


Commands

  • "new research: <topic>" - Start interactive research doc
  • "deep research: <topic>" - Start async deep research
  • "show doc" / "show research" - Display current research file
  • "summarize" - Synthesis checkpoint
  • "graduate" - Move research to next phase
  • "archive" - Mark as complete reference
  • "export pdf" - Export to PDF
  • "check research" - Check status of pending deep research tasks

Document Principles

  • Atomic findings - One insight per bullet
  • Link everything - Sources, docs, repos
  • Capture context - Why did we look at this?
  • Note confidence - Use qualifiers when uncertain
  • Date important findings - Especially for fast-moving topics

Setup

See SETUP.md for first-time installation of:
- parallel-research CLI
- PDF export tools (pandoc, PyMuPDF)

常见问题

5 问
答案由本站依据条目字段整理
智能体怎么安装?

在支持 Skills 的客户端里执行:skillhub install zcwl/research-agent。安装方式为未标注。装完刷新技能列表或重开客户端。

智能体支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

智能体权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

智能体收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

智能体用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

热门专题

按场景挑技能
全部专题 →

技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。