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.

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. 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_pipeline, nlm_trend_research

Notebook deletion requires confirm=true. HTTP mode binds only to loopback:

notebooklm-mcp --http --host 127.0.0.1 --port 8765

Do not expose HTTP mode directly to a network. If remote access is unavoidable, put it behind an authenticated TLS proxy and apply host-level access controls.

Operating rules

1. Verify authentication before a long workflow.
2. Confirm sources were ingested; treat partial or failed source entries honestly.
3. Ask focused questions and retain returned citation metadata.
4. Use exact notebook and artifact IDs in repeated automation.
5. Use bounded source/artifact concurrency; generation is quota-sensitive.
6. Do not delete notebooks or overwrite output without explicit user intent.
7. Do not claim a draft was published; no publishing integration exists here.
8. Return the JSON result or a faithful summary, including partial failures.

Exit codes and recovery

• 0: operation completed successfully.
• 2: invalid or ambiguous arguments.
• 4: authentication required.
• 1: upstream, network, generation, or other operational failure.
• 130: interrupted by the user.

Common recovery:

notebooklm-auth verify
notebooklm-auth setup                 # missing or expired session
notebooklm-skill list-artifacts --notebook NOTEBOOK_ID  # inspect a timed-out job

Use notebooklm-skill --help, notebooklm-pipeline --help, and the relevant subcommand's --help as the authoritative local command contract.

notebooklm-skill

Source-grounded NotebookLM automation for terminals and AI agents.

繁體中文

notebooklm-skill gives humans and MCP clients one consistent interface for Google NotebookLM. It creates notebooks from URLs, raw text, and local files; asks grounded questions with citation metadata; completes fast or deep web research; and generates or downloads NotebookLM artifacts.

The project is built around notebooklm-py 0.7.x and includes:

• a JSON-first core CLI;
• five end-to-end research pipelines;
• a 13-tool FastMCP server;
• profile-aware authentication and Skill installers;
• one shared compatibility layer, so CLI, pipelines, and MCP use the same behavior.

This is an unofficial integration with NotebookLM's web API. Google can change the service, availability, quotas, or artifact behavior without notice.

Quick start

Isolated source install

The installer creates a dedicated virtual environment, installs Chromium, links five commands into ~/.local/bin, and installs the Claude Code Skill using the standard directory layout.

git clone https://github.com/claude-world/notebooklm-skill.git
cd notebooklm-skill
./install.sh

notebooklm-auth setup
notebooklm-skill list

Ensure ~/.local/bin is on PATH.

PyPI or uvx

# Persistent virtual environment
python3 -m venv .venv
source .venv/bin/activate
python -m pip install notebooklm-skill
python -m playwright install chromium
notebooklm-auth setup

# Or run without a persistent install
uvx --from notebooklm-skill notebooklm-auth setup
uvx --from notebooklm-skill notebooklm-skill list

Direct upstream login is also available:

uvx --from notebooklm-py notebooklm login

Sessions are profile-aware. Select one with --profile NAME before a CLI subcommand, or set NOTEBOOKLM_PROFILE.

To use the locally installed Google Chrome instead of bundled Chromium:

notebooklm-auth setup --browser chrome --fresh

Core CLI

All successful commands print structured JSON to stdout. Diagnostics go to stderr; authentication errors return exit code 4 and argument errors return exit code 2.

# Mixed-source ingestion with truthful per-source outcomes
notebooklm-skill create \
  --title "Research" \
  --sources https://example.com/article \
  --files ./paper.pdf \
  --text-sources "Interview notes" \
  --strict

notebooklm-skill ask \
  --notebook "Research" \
  --query "Which conclusions have the strongest evidence?"

notebooklm-skill research \
  --notebook "Research" \
  --query "Recent independent evaluations" \
  --mode deep --max-sources 10

notebooklm-skill generate \
  --notebook "Research" \
  --type slides --lang zh-TW \
  --output ./output/deck.pptx --output-format pptx

notebooklm-skill list-artifacts --notebook "Research" --type slides

Commands resolve an exact ID, unique title, or unique title substring. Use IDs for repeatable automation. Deletes require --yes; downloads refuse existing files or symlinks unless an explicit safe overwrite is requested with --force.

Artifact types

Use notebooklm-skill generate --help for the live option matrix. Long-running generations support --no-wait, and later downloads can select --artifact-id.

Pipelines

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

notebooklm-pipeline research-to-social \
  --files ./brief.pdf --platform linkedin --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 the optional trend-pulse command. Pipelines return drafts and local artifacts; they do not publish to social platforms or remote CMSs.

MCP server

The default stdio transport is suitable for Claude Code, Cursor, and other MCP clients:

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

The 13 tools cover notebook CRUD, mixed sources, grounded chat, summaries, artifact generation/list/download, full research lifecycles, research pipelines, and trend research. Notebook deletion requires confirm=true.

Optional HTTP mode is deliberately loopback-only:

notebooklm-mcp --http --host 127.0.0.1 --port 8765

Do not expose it directly to a network. See SECURITY.md.

Additional commands

notebooklm-install-skill --scope project
notebooklm-auth --profile work verify

Development

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev]'
ruff check .
ruff format --check .
mypy scripts mcp_server
pytest --cov --cov-report=term-missing
python -m build
twine check dist/*

More detail: setup guide, Skill instructions, API compatibility notes, and changelog.

License

MIT