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gemini-deep-research-notionGemini deep 研究 Notion

Agent Skill

用于处理 Notion 页面、数据库、工作区内容和结构化记录。它适合让 Agent 查询知识库、整理页面内容、创建记录或把外部信息同步到 Notion。使用时需要确认集成是否已被授权到目标页面或数据库,并区分读取、追加和覆盖更新;涉及批量写入或修改数据库属性时,应先核对字段名称、属性类型和目标页面。

总安装

7,078

周安装

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GitHub Stars

公开资料未说明

下载量

2,313
OpenClaw

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

MIT-0

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:gemini-deep-research-notion(Gemini deep 研究 Notion)
来源仓库:https://github.com/palmpalm7/gemini-deep-research-notion
安装命令:
openclaw skills install gemini-deep-research-notion
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install gemini-deep-research-notion

简介

gemini-deep-research-notion 触发深度研究并将结果同步至 Notion 数据库。

  • 适合知识管理与团队协作场景,实现研究内容与工作流的集成。
  • 安装前需授权 Notion 集成,并指定目标页面或数据库字段。
  • 涉及批量写入时应核对字段类型,防止数据格式不匹配导致失败。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
gemini-deep-research
description
Trigger Gemini Deep Research via browser and save results to Notion. Use when the user asks to "deep research" a topic, says "gemini deep research", or wants an in-depth research report. Execute ALL steps in the main session (browser tool requires main session access).

Gemini Deep Research → Notion

Execution Mode

Run ALL steps in the MAIN SESSION. Do NOT spawn a subagent.

The browser tool (OpenClaw managed profile) is only available in the main session. Subagents cannot access the browser, so all browser automation must happen here.

Reply first: "🔬 Deep Research starting for: [topic]. This takes ~25 min. I'll update you when done."

Then execute all phases below sequentially.


Instructions

Complete ALL steps below in the main session.

Phase 1: Trigger Deep Research

  1. browser action=open profile=openclaw targetUrl="https://gemini.google.com/app"
  2. Snapshot, find the text input, type the research query. Always prepend "请用中文回答。" to the query so the research output is in Chinese.
  3. Click "工具" (Tools) button (has page_info icon) → click "Deep Research" in the menu
  4. Click Send to submit the query
  5. Wait for research plan to appear (~10s), then click "Start research" / "开始研究" button

- If snapshot-click doesn't work, use JS: (() => { var btn = Array.from(document.querySelectorAll('button')).find(b => /Start research|开始研究/.test(b.textContent.trim())); if (btn) { btn.click(); return 'clicked'; } return 'not found'; })()

  1. Verify research started: button should be disabled, status shows "Researching X websites..." or "正在研究..."
  2. Save the conversation URL from the browser

Phase 2: Wait for Completion

  1. Run exec("sleep 1200") (20 minutes) + process(poll, timeout=1200000)
  2. After waking, check status via JS: (() => { var el = document.querySelectorAll('message-content')[1]; return el ? el.innerText.substring(0, 200) : 'NOT_FOUND'; })()
  3. Look for completion signals: "I've completed your research" or "已完成"
  4. If still running, sleep another 600s and check again (max 2 retries)
  5. If failed/stuck after retries, announce the failure and exit

Phase 3: Extract Report

  1. Count message-content elements: document.querySelectorAll('message-content').length
  2. The research report is in the LAST message-content element (usually index 2)
  3. Get total length: document.querySelectorAll('message-content')[2]?.innerText?.length
  4. Extract in 8000-char chunks using substring: document.querySelectorAll('message-content')[N]?.innerText?.substring(START, END)
  5. Concatenate all chunks into the full report text
  6. Save to a temp file: write full report to /tmp/deep_research_<timestamp>.md

Phase 4: Export to Notion

Parent page ID: 31a4cfb5-c92b-809f-9d8a-dd451718a017 (Deep Research Database)

  1. Read the Notion API key: cat ~/.config/notion/api_key
  2. Parse the report into Notion blocks:

- Lines starting with # → heading_2/heading_3 blocks - Bullet points → bulleted_list_item blocks - Regular text → paragraph blocks - Add a callout at top: "🔬 Generated by Gemini Deep Research on YYYY-MM-DD" - Split rich_text at 2000 chars

  1. Create the page via Notion API:
   curl -s -X POST "https://api.notion.com/v1/pages" \
     -H "Authorization: Bearer $NOTION_KEY" \
     -H "Notion-Version: 2025-09-03" \
     -H "Content-Type: application/json" \
     -d '{"parent":{"page_id":"31a4cfb5-c92b-809f-9d8a-dd451718a017"},"icon":{"type":"emoji","emoji":"🔬"},"properties":{"title":{"title":[{"text":{"content":"TOPIC"}}]}},"children":[BLOCKS]}'
  1. If >100 blocks, append remaining via PATCH to /v1/blocks/{page_id}/children
  2. Rate limit: wait 0.5s between batch requests

Phase 5: Announce

Report back with:

  • Research topic
  • Brief summary (2-3 key findings)
  • Notion page URL: https://www.notion.so/<page_id_without_dashes>

Notes

  • Always use profile="openclaw" for browser
  • Deep Research is under "工具" (Tools) menu, NOT the model selector
  • If Gemini needs login, announce failure — user must log in manually
  • The full pipeline should complete in ~25-30 min total

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

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

OpenClaw

82.31%
按下载量换算1,904

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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