/cs:notebooklm — NotebookLM browser automation. Action-routing intake (Q1: read / add source / Studio output / create new) + per-action Q2-Q4 branching. Fire-an
复制下面这句话,粘贴给 Claude Code、Codex、Cursor 等 AI 编程工具,它会读取安装说明并在你确认后完成安装。
请阅读 https://ai.atlankj.com/install/asset/gh-claude-skills-e6b52f28cb48 ,按照其中的说明把「cs-notebooklm」安装到你(当前 AI 工具)中。执行前先告诉我将运行的命令和写入的位置,等我确认。
查看 AI 将读取的安装说明正在读取 GitHub 原文…
内容来自 GitHub 原始文件,由原作者维护。在 GitHub 查看
Command: /cs:notebooklm
The cs-notebooklm persona controls Google NotebookLM via browser automation across 4 core actions.
Requires browser automation environment. Works in:
Does NOT work in:
| Q | Asks | Notes |
|---|---|---|
| Q1 | Action: read / add source / Studio output / create new | Forcing — refuses to start without commitment |
| Q2 | Notebook name or URL (actions 1-3) OR title for new notebook (action 4) | Drives navigation |
| Q3 | Action-specific parameter (question text / source type / Studio output type / initial sources) | Branches per Q1 |
| Q4 | Studio custom prompt detail | Asked only if Q1=3 (Studio); mandatory |
Most invocations stop at Q3. Q4 only fires for Studio generation.
Per action:
| Action | Result |
|---|---|
| Read/Extract | Clean response from notebook chat (not raw dump) |
| Add Sources | Confirmation of ingestion (with screenshot) |
| Studio Output | Confirmation that generation started + "NotebookLM will notify you when ready" — fire-and-notify |
| Create New | New notebook URL + confirmation of initial sources added |
All 9 types supported:
Default Studio prompts produce mediocre output. The skill ALWAYS opens the customization menu and writes a detailed custom prompt before submitting.
Examples per output type:
| Output | Example custom prompt |
|---|---|
| Audio Overview | "Two-host conversation for a non-technical executive, 8-10 min, focus on business implications not technical depth" |
| Infographic | "Decision-tree style, action-oriented, 6 panels max, monochrome navy" |
| Study Guide | "Undergrad-level, definitions + 3 practice questions per concept" |
| Slides | "12 slides max, 1-2 sentences per slide, presenter notes with examples per slide" |
# Step 0: environment check (silent if available; halt if not)
# Phase 0 intake (Q1 + Q2 minimum; Q3-Q4 branch per action)
python ../skills/notebooklm/scripts/action_router.py \
--action read_extract --notebook "Q3 prep" --question "what are the latest trends?"
# Studio output flow includes custom prompt generation:
python ../skills/notebooklm/scripts/custom_prompt_template_generator.py \
--output-type infographic --audience executive --length compact
# Async classification (for "should I wait or fire-and-notify?")
python ../skills/notebooklm/scripts/async_action_classifier.py --action audio_overview
# Returns: FIRE_AND_NOTIFY (5-10 min generation)
# Execute action via browser automation (screenshot → find → click → verify)
# Return clean summary
cs-notebooklmnotebooklmmegaprompts/03-notebooklm-megaprompt.md (maintainer-local draft spec — gitignored, not in the public repo)/cs:pulse, /cs:litreview, /cs:grants, /cs:dossier, /cs:patent, /cs:syllabusVersion: 1.0.0
Source: Path-B direct conversion of megaprompts/03-notebooklm-megaprompt.md