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notebooklmNotebookLM 笔记研究

Agent Skill

notebooklm 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

722

周安装

31

GitHub Stars

6

下载量

253
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:notebooklm(NotebookLM 笔记研究)
来源仓库:https://github.com/tiangong-ai/skills
仓库路径:skills/notebooklm
安装命令:
npx skills add https://github.com/tiangong-ai/skills --skill notebooklm
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/tiangong-ai/skills --skill notebooklm

简介

notebooklm 用于查找、检索和筛选相关信息。

  • 适合在关键词搜索或任务场景中快速定位候选结果。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装命令:npx skills add https://github.com/tiangong-ai/skills --skill notebooklm
  • 安装前建议确认权限范围和维护状态。

SKILL.md

NotebookLM CLI Wrapper (Python)

Required parameters

  • python3 available.
  • notebooklm-py installed (CLI binary: notebooklm).
  • NotebookLM authenticated (login).

Quick start

  • Wrapper script: scripts/notebooklm.py.
  • Command form: python3 {baseDir}/scripts/notebooklm.py <command> [args...].
python3 {baseDir}/scripts/notebooklm.py login
python3 {baseDir}/scripts/notebooklm.py list
python3 {baseDir}/scripts/notebooklm.py use <notebook_id>
python3 {baseDir}/scripts/notebooklm.py status
python3 {baseDir}/scripts/notebooklm.py ask "Summarize the key takeaways" --notebook <notebook_id>

Output guidance

  • Prefer --json for machine-readable output where supported.
  • Long-running waits are handled by native commands like:

- source wait - artifact wait - research wait

⚡ Sub-Agent Delegation (Anti-Blocking)

Problem

NotebookLM operations like source wait, artifact wait, research wait, generate slide-deck, and source add-research can take minutes to complete. Running them in the main session blocks the conversation.

Strategy

For any operation expected to take >30 seconds, delegate to a sub-agent via sessions_spawn:

  1. Main session: Acknowledge the user's request, then spawn a sub-agent with a clear task description.
  2. Sub-agent: Executes the long-running NotebookLM commands, waits for completion, and reports back.
  3. Main session: Remains responsive. The sub-agent auto-announces completion.

Which operations to delegate

OperationDelegate?Reason
login, status, list, use, clear❌ NoFast (<5s)
ask (chat)❌ NoUsually fast (~10s)
source list, source get, note list❌ NoFast reads
source add (URL/text)⚠️ MaybeFast to submit, but source wait after is slow
source add-research✅ YesDeep research can take 2-5 min
source wait✅ YesPolling wait, unpredictable duration
generate slide-deck + artifact wait✅ YesGeneration takes 1-5 min
research wait✅ YesCan take several minutes
download slide-deck⚠️ MaybeUsually fast, but can be slow for large files
Multi-step workflows (add sources → wait → generate → wait → download)✅ YesCompound long tasks

How to spawn

sessions_spawn:
  task: |
    You are a NotebookLM task runner. Execute the following NotebookLM operations
    and report results when done.

    Notebook ID: <notebook_id>
    Commands to run (in order):
    1. <command 1>
    2. <command 2>
    ...

    Use the CLI wrapper: python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py
    Prefer --json output where supported.
    If any step fails, report the error and stop.
    When complete, summarize what was accomplished and any output files created.
  mode: run
  label: notebooklm-<short-description>

Example: Generate slide deck

User: "帮我用 notebook X 生成一个 PPT"

Main session response:

好的,我派了一个后台任务去生成 PPT,完成后会通知你 ✧

Spawn:

sessions_spawn:
  task: |
    NotebookLM task: Generate a slide deck from notebook.

    Steps:
    1. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py generate slide-deck "Create a comprehensive slide deck" --notebook <id>
    2. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py artifact wait <artifact_id> --notebook <id> --timeout 600 --json
    3. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py download slide-deck ./output.pptx --notebook <id> --latest --format pptx

    Report: artifact details, file path, any errors.
  mode: run
  label: notebooklm-slide-deck

Example: Add research source

User: "在 notebook Y 里加一个关于碳足迹的深度研究"

Spawn:

sessions_spawn:
  task: |
    NotebookLM task: Add deep research source.

    Steps:
    1. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py source add-research "碳足迹最新研究进展" --mode deep --notebook <id>
    2. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py research wait --notebook <id> --timeout 600
    3. python3 ~/.openclaw/skills/notebooklm-Invoke/scripts/notebooklm.py source list --notebook <id> --json

    Report: research status, new sources added, any errors.
  mode: run
  label: notebooklm-research

Guidelines

  • Always tell the user you're delegating to a background task before spawning.
  • Use mode: run (one-shot) — no need for persistent sessions.
  • Use descriptive labels like notebooklm-slide-deck, notebooklm-research-carbon for easy tracking.
  • Include all context in the task — the sub-agent has no conversation history.
  • Error handling: Instruct the sub-agent to report errors clearly so you can relay them.
  • File paths: Use absolute paths for output files so the main session can find them.
  • Compound workflows: Bundle related steps (add → wait → generate → wait → download) into a single sub-agent task rather than spawning multiple.

PPT generation policy

  • A single generated slide deck should target at most 15 pages.
  • If user requirements exceed 15 pages, split into multiple decks (e.g., Part 1/2/3) and generate separately.
  • After generation, provide downloadable .pptx output when possible:

- download slide-deck... --format pptx

References

  • README.md (installation, requirements, troubleshooting)
  • QUICKSTART_CN.md(中文快速上手)
  • references/cli-commands.md

Assets

  • None.

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

38.67%
按下载量换算98

Claude

27.67%
按下载量换算70

Cursor

18.17%
按下载量换算46

Gemini CLI

10.15%
按下载量换算26

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/tiangong-ai/skills --skill notebooklm 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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