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...]

bash

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

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

Which operations to delegate

Operation	Delegate?	Reason
`login` , `status` , `list` , `use` , `clear`	❌ No	Fast (<5s)
`ask` (chat)	❌ No	Usually fast (~10s)
`source list` , `source get` , `note list`	❌ No	Fast reads
`source add` (URL/text)	⚠️ Maybe	Fast to submit, but `source wait` after is slow
`source add-research`	✅ Yes	Deep research can take 2-5 min
`source wait`	✅ Yes	Polling wait, unpredictable duration
`generate slide-deck` + `artifact wait`	✅ Yes	Generation takes 1-5 min
`research wait`	✅ Yes	Can take several minutes
`download slide-deck`	⚠️ Maybe	Usually fast, but can be slow for large files
Multi-step workflows (add sources → wait → generate → wait → download)	✅ Yes	Compound 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.

notebooklm

NPX Install

Tags

SKILL.md Content