notebooklm

teng-lin/notebooklm-py · updated Apr 8, 2026

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$npx skills add https://github.com/teng-lin/notebooklm-py --skill notebooklm
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summary

Programmatic access to Google NotebookLM with full artifact generation, source management, and download capabilities.

  • Create notebooks, add sources (URLs, PDFs, YouTube, audio, video, images), and chat with content via CLI or Python API
  • Generate all artifact types: podcasts, videos, slide decks, infographics, reports, quizzes, flashcards, mind maps, and data tables in multiple formats
  • Supports parallel agent workflows with explicit notebook IDs, subagent patterns for long-running ope
skill.md

NotebookLM Automation

Complete programmatic access to Google NotebookLM—including capabilities not exposed in the web UI. Create notebooks, add sources (URLs, YouTube, PDFs, audio, video, images), chat with content, generate all artifact types, and download results in multiple formats.

Installation

From PyPI (Recommended):

pip install notebooklm-py

From GitHub (use latest release tag, NOT main branch):

# Get the latest release tag (using curl)
LATEST_TAG=$(curl -s https://api.github.com/repos/teng-lin/notebooklm-py/releases/latest | grep '"tag_name"' | cut -d'"' -f4)
pip install "git+https://github.com/teng-lin/notebooklm-py@${LATEST_TAG}"

⚠️ DO NOT install from main branch (pip install git+https://github.com/teng-lin/notebooklm-py). The main branch may contain unreleased/unstable changes. Always use PyPI or a specific release tag, unless you are testing unreleased features.

Skill install methods:

  • notebooklm skill install installs this skill into the supported local agent directories managed by the CLI.
  • npx skills add teng-lin/notebooklm-py installs this skill from the GitHub repository into compatible agent skill directories.
  • If you are already reading this file inside an agent skill directory, the skill is already installed. You only need the Python package and authentication below.

CLI-managed install:

notebooklm skill install

Prerequisites

IMPORTANT: Before using any command, you MUST authenticate:

notebooklm login          # Opens browser for Google OAuth
notebooklm list           # Verify authentication works

If commands fail with authentication errors, re-run notebooklm login.

CI/CD, Multiple Accounts, and Parallel Agents

For automated environments, multiple accounts, or parallel agent workflows:

Variable Purpose
NOTEBOOKLM_HOME Custom config directory (default: ~/.notebooklm)
NOTEBOOKLM_PROFILE Active profile name (default: default)
NOTEBOOKLM_AUTH_JSON Inline auth JSON - no file writes needed

CI/CD setup: Set NOTEBOOKLM_AUTH_JSON from a secret containing your storage_state.json contents.

Multiple accounts: Use named profiles (notebooklm profile create work, then notebooklm -p work login). Alternatively, use different NOTEBOOKLM_HOME directories per account.

Parallel agents: The CLI stores notebook context in a shared file (~/.notebooklm/context.json). Multiple concurrent agents using notebooklm use can overwrite each other's context.

Solutions for parallel workflows:

  1. Always use explicit notebook ID (recommended): Pass -n <notebook_id> (for wait/download commands) or --notebook <notebook_id> (for others) instead of relying on use
  2. Per-agent isolation via profiles: export NOTEBOOKLM_PROFILE=agent-$ID (each profile gets its own context file)
  3. Per-agent isolation via home: Set unique NOTEBOOKLM_HOME per agent: export NOTEBOOKLM_HOME=/tmp/agent-$ID
  4. Use full UUIDs: Avoid partial IDs in automation (they can become ambiguous)

Agent Setup Verification

Before starting workflows, verify the CLI is ready:

  1. notebooklm status → Should show "Authenticated as: email@..."
  2. notebooklm list --json → Should return valid JSON (even if empty notebooks list)
  3. If either fails → Run notebooklm login

When This Skill Activates

Explicit: User says "/notebooklm", "use notebooklm", or mentions the tool by name

Intent detection: Recognize requests like:

  • "Create a podcast about [topic]"
  • "Summarize these URLs/documents"
  • "Generate a quiz from my research"
  • "Turn this into an audio overview"
  • "Create flashcards for studying"
  • "Generate a video explainer"
  • "Make an infographic"
  • "Create a mind map of the concepts"
  • "Download the quiz as markdown"
  • "Add these sources to NotebookLM"

Autonomy Rules

Run automatically (no confirmation):

  • notebooklm status - check context
  • notebooklm auth check - diagnose auth issues
  • notebooklm list - list notebooks
  • notebooklm source list - list sources
  • notebooklm artifact list - list artifacts
  • notebooklm language list - list supported languages
  • notebooklm language get - get current language
  • notebooklm language set - set language (global setting)
  • notebooklm artifact wait - wait for artifact completion (in subagent context)
  • notebooklm source wait - wait for source processing (in subagent context)
  • notebooklm research status - check research status
  • notebooklm research wait - wait for research (in subagent context)
  • notebooklm use <id> - set context (⚠️ SINGLE-AGENT ONLY - use -n flag in parallel workflows)
  • notebooklm create - create notebook
  • notebooklm ask "..." - chat queries (without --save-as-note)
  • notebooklm history - display conversation history (read-only)
  • notebooklm source add - add sources
  • notebooklm profile list - list profiles
  • notebooklm profile create - create profile
  • notebooklm profile switch - switch active profile
  • notebooklm doctor - check environment health

Ask before running:

  • notebooklm delete - destructive
  • notebooklm generate * - long-running, may fail
  • notebooklm download * - writes to filesystem
  • notebooklm artifact wait - long-running (when in main conversation)
  • notebooklm source wait - long-running (when in main conversation)
  • notebooklm research wait - long-running (when in main conversation)
  • notebooklm ask "..." --save-as-note - writes a note
  • notebooklm history --save - writes a note

Quick Reference

Task Command
Authenticate notebooklm login
Diagnose auth issues notebooklm auth check
Diagnose auth (full) notebooklm auth check --test
List notebooks notebooklm list
Create notebook notebooklm create "Title"
Set context notebooklm use <notebook_id>
Show context notebooklm status
Add URL source notebooklm source add "https://..."
Add file notebooklm source add ./file.pdf
Add YouTube notebooklm source add "https://youtube.com/..."
List sources notebooklm source list
Delete source by ID notebooklm source delete <source_id>
Delete source by exact title notebooklm source delete-by-title "Exact Title"
Wait for source processing notebooklm source wait <source_id>
Web research (fast) notebooklm source add-research "query"
Web research (deep) notebooklm source add-research "query" --mode deep --no-wait
Check research status notebooklm research status
Wait for research notebooklm research wait --import-all
Chat notebooklm ask "question"
Chat (specific sources) notebooklm ask "question" -s src_id1 -s src_id2
Chat (with references) notebooklm ask "question" --json
Chat (save answer as note) notebooklm ask "question" --save-as-note
Chat (save with title) notebooklm ask "question" --save-as-note --note-title "Title"
Show conversation history notebooklm history
Save all history as note notebooklm history --save
Continue specific conversation notebooklm ask "question" -c <conversation_id>
Save history with title notebooklm history --save --note-title "My Research"
Get source fulltext notebooklm source fulltext <source_id>
Get source guide notebooklm source guide <source_id>
Generate podcast notebooklm generate audio "instructions"
Generate podcast (JSON) notebooklm generate audio --json
Generate podcast (specific sources) notebooklm generate audio -s src_id1 -s src_id2
Generate video notebooklm generate video "instructions"
Generate report notebooklm generate report --format briefing-doc
Generate report (append instructions) notebooklm generate report --format study-guide --append "Target audience: beginners"
Generate quiz notebooklm generate quiz
Revise a slide notebooklm generate revise-slide "prompt" --artifact <id> --slide 0
Check artifact status notebooklm artifact list
Wait for completion notebooklm artifact wait <artifact_id>
Download audio notebooklm download audio ./output.mp3
Download video notebooklm download video ./output.mp4
Download slide deck (PDF) notebooklm download slide-deck ./slides.pdf
Download slide deck (PPTX) notebooklm download slide-deck ./slides.pptx --format pptx
Download report notebooklm download report ./report.md
Download mind map notebooklm download mind-map ./map.json
Download data table notebooklm download data-table ./data.csv
Download quiz notebooklm download quiz quiz.json
Download quiz (markdown) notebooklm download quiz --format markdown quiz.md
Download flashcards notebooklm download flashcards cards.json
Download flashcards (markdown) notebooklm download flashcards --format markdown cards.md
Delete notebook notebooklm notebook delete <id>
List languages notebooklm language list
Get language notebooklm language get
Set language notebooklm language set zh_Hans
List profiles notebooklm profile list
Create profile notebooklm profile create work
Switch profile notebooklm profile switch work
Delete profile notebooklm profile delete old
Rename profile notebooklm profile rename old new
Use profile (one-off) notebooklm -p work list
Health check notebooklm doctor
Health check (auto-fix) notebooklm doctor --fix

Parallel safety: Use explicit notebook IDs in parallel workflows. Commands supporting -n shorthand: artifact wait, source wait, research wait/status, download *. Download commands also support -a/--artifact. Other commands use --notebook. For chat, use -c <conversation_id> to target a specific conversation.

Partial IDs: Use first 6+ characters of UUIDs. Must be unique prefix (fails if ambiguous). Works for ID-based commands such as use, source delete, and wait. For exact source-title deletion, use source delete-by-title "Title". For automation, prefer full UUIDs to avoid ambiguity.

Command Output Formats

Commands with --json return structured data for parsing:

Create notebook:

$ notebooklm create "Research" --json
{"id": "abc123de-...", "title": "Research"}

Add source:

$ notebooklm source add "https://example.com" --json
{"source_id": "def456...", "title": "Example", "status": "processing"}

Generate artifact:

$ notebooklm generate audio "Focus on key points" --json
{"task_id": "xyz789...", "status": "pending"}

Chat with references:

$ notebooklm ask "What is X?" --json
{"answer": "X is... [1] [2]", "conversation_id": "...", "turn_number": 1, "is_follow_up": false, "references": [{"source_id": "abc123...", "citation_number": 1, "cited_text": "Relevant passage from source..."}, {"source_id": "def456...", "citation_number": 2, "cited_text": "Another passage..."}]}

Source fulltext (get indexed content):

$ notebooklm source fulltext <source_id> --json
{"source_id": "...", "title": "...", "char_count": 12345, "content": "Full indexed text..."}

Understanding citations: The cited_text in references is often a snippet or section header, not the full quoted passage. The start_char/end_char positions reference NotebookLM's internal chunked index, not the raw fulltext. Use SourceFulltext.find_citation_context() to locate citations:

fulltext = await client.sources.get_fulltext(notebook_id, ref.source_id)
matches = fulltext.find_citation_context(ref.cited_text)  # Returns list[(context, position)]
if matches:
    context, pos = matches[0]  # First match; check len(matches) > 1 for duplicates

Extract IDs: Parse the id, source_id, or task_id field from JSON output.

Generation Types

All generate commands support:

  • -s, --source to use specific source(s) instead of all sources
  • --language to set output language (defaults to configured language or 'en')
  • --json for machine-readable output (returns task_id and status)
  • --retry N to automatically retry on rate limits with exponential backoff
Type Command Options Download
Podcast generate audio --format [deep-dive|brief|critique|debate], --length [short|default|long] .mp3
Video generate video --format [explainer|brief], --style [auto|classic|whiteboard|kawaii|anime|watercolor|retro-print|heritage|paper-craft] .mp4
Slide Deck generate slide-deck --format [detailed|presenter], --length [default|short] .pdf / .pptx
Slide Revision generate revise-slide "prompt" --artifact <id> --slide N --wait, --notebook (re-downloads parent deck)
Infographic generate infographic --orientation [landscape|portrait|square], --detail [concise|standard|detailed], --style [auto|sketch-note|professional|bento-grid|editorial|instructional|bricks|clay|anime|kawaii|scientific] .png
Report generate report --format [briefing-doc|study-guide|blog-post|custom], --append "extra instructions" .md
Mind Map generate mind-map (sync, instant) .json
Data Table generate data-table description required .csv
Quiz generate quiz --difficulty [easy|medium|hard], --quantity [fewer|standard|more] .json/.md/.html
Flashcards generate flashcards --difficulty [easy|medium|hard], --quantity [fewer|standard|more] .json/.md/.html

Features Beyond the Web UI

These capabilities are available via CLI but not in NotebookLM's web interface:

Feature Command Description
Batch downloads download <type> --all Download all artifacts of a type at once
Quiz/Flashcard export download quiz --format json Export as JSON, Markdown, or HTML (web UI only shows interactive view)
Mind map extraction download mind-map Export hierarchical JSON for visualization tools
Data table export download data-table Download structured tables as CSV
Slide deck as PPTX download slide-deck --format pptx Download slide deck as editable .pptx (web UI only offers PDF)
Slide revision generate revise-slide "prompt" --artifact <id> --slide N Modify individual slides with a natural-language prompt
Report template append generate report --format study-guide --append "..." Append custom instructions to built-in format templates without losing the format type
Source fulltext source fulltext <id> Retrieve the indexed text content of any source
Save chat to note ask "..." --save-as-note / history --save Save Q&A answers or conversation history as notebook notes
Programmatic sharing share commands Manage sharing permissions without the UI

Common Workflows

Research to Podcast (Interactive)

Time: 5-10 minutes total

  1. notebooklm create "Research: [topic]"if fails: check auth with notebooklm login
how to use notebooklm

How to use notebooklm on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add notebooklm
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/teng-lin/notebooklm-py --skill notebooklm

The skills CLI fetches notebooklm from GitHub repository teng-lin/notebooklm-py and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/notebooklm

Reload or restart Cursor to activate notebooklm. Access the skill through slash commands (e.g., /notebooklm) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.536 reviews
  • Pratham Ware· Dec 24, 2024

    notebooklm reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Anika Gupta· Dec 16, 2024

    Keeps context tight: notebooklm is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Noor Sethi· Nov 19, 2024

    Solid pick for teams standardizing on skills: notebooklm is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 15, 2024

    I recommend notebooklm for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Noor Garcia· Oct 10, 2024

    notebooklm has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 6, 2024

    Useful defaults in notebooklm — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Piyush G· Sep 25, 2024

    notebooklm has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Oshnikdeep· Sep 21, 2024

    notebooklm is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Harper Torres· Sep 17, 2024

    notebooklm reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chen Sanchez· Sep 13, 2024

    Registry listing for notebooklm matched our evaluation — installs cleanly and behaves as described in the markdown.

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