/notebooklm-skill

name: notebooklm-research
description: >
  Automate source-grounded research with Google NotebookLM. Create notebooks from
  URLs, text, or local files; ask cited questions; run fast or deep web research;
  create articles and social drafts; and generate or download audio, video,
  cinematic video, slides, reports, study guides, quizzes, flashcards, mind maps,
  infographics, and data tables. Use when a user asks for NotebookLM, cited source
  analysis, research-to-content workflows, podcasts, slides, study material,
  artifact generation, RSS digests, or trend research.
license: MIT
compatibility: >
  Requires Python 3.10+, the notebooklm-skill package (pip install or uvx), network
  access, and a Google account signed in to NotebookLM with notebooklm-auth setup.

NotebookLM Research

Use the installed commands or the 13 MCP tools to turn user-provided sources into grounded answers and NotebookLM artifacts. Commands emit JSON on stdout and progress or diagnostics on stderr, so preserve stdout when another tool will consume it.

This integration uses NotebookLM's browser session and unofficial web API through `notebooklm-py`. Do not promise that Google-side availability, quotas, or generation time are stable.

Authentication

Prefer the profile-aware helper:

notebooklm-auth setup
notebooklm-auth verify

Use `notebooklm-auth setup --browser chrome --fresh` when the user explicitly wants the locally installed Google Chrome instead of bundled Chromium.

For a zero-install login:

uvx --from notebooklm-py notebooklm login

Profiles are supported through `--profile NAME` before the subcommand or through `NOTEBOOKLM_PROFILE`. Current sessions are normally stored below `~/.notebooklm/profiles/<profile>/storage_state.json`; never read, print, copy, or commit that file. If authentication expires, run setup again.

Core CLI

Create a notebook from mixed sources:

notebooklm-skill create \
  --title "AI safety evidence" \
  --sources https://example.com/article https://youtu.be/example \
  --files ./paper.pdf \
  --text-sources "A user-supplied observation" \
  --strict

Inspect and ask:

notebooklm-skill list
notebooklm-skill list-sources --notebook "AI safety evidence"
notebooklm-skill summarize --notebook "AI safety evidence"
notebooklm-skill ask --notebook "AI safety evidence" --query "What findings conflict?"

Add exactly one source:

notebooklm-skill add-source --notebook "AI safety evidence" --url https://example.com/new
notebooklm-skill add-source --notebook "AI safety evidence" --file ./appendix.docx
notebooklm-skill add-source --notebook "AI safety evidence" \
  --text "Raw notes" --text-title "Interview notes"

Run NotebookLM web research and import results:

notebooklm-skill research \
  --notebook "AI safety evidence" \
  --query "Recent empirical evaluations" \
  --mode deep --max-sources 10

Use `--no-wait` for a task ID without waiting. Use `--no-import-results` when the research results should not become notebook sources.

Notebook titles may be used only when they resolve uniquely. Prefer IDs in automation.

Artifact generation

Supported canonical types:

`audio`, `video`, `cinematic`, `slides`, `report`, `study-guide`, `quiz`, `flashcards`, `mind-map`, `infographic`, `data-table`.

Generate and optionally download in one operation:

notebooklm-skill generate \
  --notebook "AI safety evidence" \
  --type slides --lang zh-TW \
  --slide-format presenter-slides \
  --output ./output/deck.pptx --output-format pptx

Long media jobs can be detached and downloaded later by exact ID:

notebooklm-skill generate --notebook NOTEBOOK_ID --type audio --no-wait
notebooklm-skill list-artifacts --notebook NOTEBOOK_ID --type audio
notebooklm-skill download --notebook NOTEBOOK_ID --type audio \
  --artifact-id ARTIFACT_ID --output ./output/podcast.m4a

Convenience commands:

notebooklm-skill podcast --notebook NOTEBOOK_ID --output podcast.m4a
notebooklm-skill qa --notebook NOTEBOOK_ID --difficulty hard --output quiz.json

Generation supports per-type options, including `--video-format short` for a vertical short video (fixed style, so no `--video-style` or `--style-prompt`) and `--quantity more` for larger quizzes and flashcard sets. Inspect the live contract before composing an unfamiliar call:

notebooklm-skill generate --help

Existing output files and symlinks are rejected. Use `--force` only when the user explicitly wants an overwrite. Quiz and flashcard downloads support JSON, Markdown, or HTML; slide downloads support PDF or PPTX.

High-level pipelines

notebooklm-pipeline research-to-article \
  --sources https://example.com/a https://example.com/b \
  --title "Evidence review" --language zh-TW --audience "engineers"

notebooklm-pipeline research-to-social \
  --sources https://example.com/a --platform threads --variants 3

notebooklm-pipeline batch-digest \
  --rss https://example.com/feed.xml --max-entries 20 --qa-count 5

notebooklm-pipeline generate-all \
  --files ./paper.pdf --types audio slides report mind-map \
  --output-dir ./output --artifact-concurrency 2

`trend-to-content` requires a separately installed `trend-pulse` command. Override its executable safely with `TREND_PULSE_CMD`; the integration does not invoke a shell.

Pipelines create drafts and local artifacts. They do not publish to social networks, CMS products, or other remote destinations.

MCP server

Start stdio mode for an MCP client:

notebooklm-mcp

Example configuration:

{
  "mcpServers": {
    "notebooklm": {
      "command": "uvx",
      "args": ["--from", "notebooklm-skill", "notebooklm-mcp"]
    }
  }
}

Available tools:

• `nlm_create_notebook`, `nlm_list`, `nlm_delete`
• `nlm_add_source`, `nlm_list_sources`
• `nlm_ask`, `nlm_summarize`
• `nlm_generate`, `nlm_download`, `nlm_list_artifacts`
• `nlm_research`, `nlm_research_pi