NotebookLM Automation

Automate Google NotebookLM: create notebooks, add sources, chat with content, generate artifacts (podcasts, videos, quizzes), and download results.

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:

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

Multiple accounts: 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: Set unique NOTEBOOKLM_HOME per agent: export NOTEBOOKLM_HOME=/tmp/agent-$ID
3. 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"
• "Add these sources to NotebookLM"
• "Create a notebook from [Twitter/X link]"
• "Extract content from this [social media post]"

Progressive fallback strategy (graceful degradation):

1. First attempt: Regular URL addition via notebooklm source add "url" (always try first)
2. Detect failure conditions: Monitor for errors like:
   • Anti-crawler protection (Twitter/X, LinkedIn)
   • Login required errors
   • 403/404 access denied
   • Processing failures/timeouts
3. Graceful fallback: Only then use browser extraction:
   • Navigate via Playwright
   • Extract content using JavaScript
   • Save to /tmp/ before adding to NotebookLM
4. User notification: Always inform user of the method used:
   • "✓ Added via direct URL" (success with regular method)
   • "⚠️ Required browser extraction (anti-crawler)" (fallback used)

Autonomy Rules

Run automatically (no confirmation):

• notebooklm status - check context
• notebooklm list - list notebooks
• notebooklm source list - list sources
• notebooklm artifact list - list artifacts
• 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
• notebooklm source add - add sources

Smart Decision Framework (Pragmatic Approach)

Installation Status System:

# Installation flag - prevents redundant checks
INSTALL_FLAG="$HOME/.cache/notebooklm/.playwright_installed"

# Function to check installation status (only once per session)
check_install_status() {
  # Fast check only if not explicitly checking
  if [[ "$1" == "--check-install-only" ]]; then
    if [ -f "$INSTALL_FLAG" ]; then
      echo "✓ Playwright is marked as installed"
      return 0
    else
      echo "⚠️ Playwright not marked, checking installation..."
      # Here you have options:
      # Option A: Quick check (try once)
      # Option B: Full installation verification
      
      # Option A (recommended): Quick check
      if timeout 3 python3 -c "import playwright" 2>/dev/null; then
        echo "✓ Playwright Python library installed"
        touch "$INSTALL_FLAG"
        return 0
      fi
      
      # Option B: Full verification (if needed)
      # pip3 install playwright
      # python3 -m playwright install chromium
  fi
}

Platform and Content Detection:

# Function: Platform type detection
detect_platform_type() {
  local url="$1"
  
  # Twitter/X: Always anti-crawler
  if [[ "$url" =~ (twitter\.com|x\.com) ]]; then
    echo "ANTICRAWLER_TWITTER"
    return 0
  
  # Other known anti-crawler platforms
  elif [[ "$url" =~ (linkedin\.com|facebook\.com|instagram\.com) ]]; then
    echo "ANTICRAWLER_OTHER"
    return 1
  
  # Open web (could support direct addition)
  else
    echo "OPEN_WEB"
    return 2
  fi
}

• Browser-based extraction for anti-crawler URLs (Twitter/X, LinkedIn, etc.):
  • Automatically detect protected platforms
  • Use Playwright automation to extract content
  • Save to /tmp/ before adding to NotebookLM
  • No confirmation needed for extraction process

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)

Quick Reference

Parallel safety: Use explicit notebook IDs in parallel workflows. Commands supporting -n shorthand: artifact wait, source wait, research wait/status, download *. Other commands use --notebook. For chat, use --new to start fresh conversations (avoids conversation ID conflicts).

Partial IDs: Use first 6+ characters of UUIDs. Must be unique prefix (fails if ambiguous). Works for: use, delete, wait commands. 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
{"artifact_id": "xyz789...", "status": "PENDING", "type": "AUDIO_OVERVIEW"}

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

Generation Types

All generate commands support -s, --source to use specific source(s) instead of all sources.

Common Workflows

Smart Decision Framework (Pragmatic Approach)

Installation Status System:

# Installation flag - prevents redundant checks
INSTALL_FLAG="$HOME/.cache/notebooklm/.playwright_installed"

# Function to check installation status (only once per session)
check_install_status() {
  # Fast check only if not explicitly checking
  if [[ "$1" == "--check-install-only" ]]; then
    if [ -f "$INSTALL_FLAG" ]; then
      echo "✓ Playwright is marked as installed"
      return 0
    else
      echo "⚠️ Playwright not marked, checking installation..."
      # Here you have options:
      # Option A: Quick check (try once)
      # Option B: Full installation verification
      
      # Option A (recommended): Quick check
      if timeout 3 python3 -c "import playwright" 2>/dev/null; then
        echo "✓ Playwright Python library installed"
        touch "$INSTALL_FLAG"
        return 0
      fi
      
      # Option B: Full verification (if needed)
      # pip3 install playwright
      # python3 -m playwright install chromium
      # touch "$INSTALL_FLAG"
      # echo "✓ Installation complete"
      # fi
  fi
  # Auto-install if missing and not checking
  if ! check_install_status && command -v python3 -c "import playwright" 2>/dev/null; then
    echo "📦 Installing Playwright..."
    pip3 install playwright
    python3 -m playwright install chromium
    touch "$INSTALL_FLAG"
    echo "✓ Installation complete"
  fi
}

Platform and Content Detection:

# Function: Platform type detection
detect_platform_type() {
  local url="$1"
  
  # Twitter/X: Always anti-crawler
  if [[ "$url" =~ (twitter\.com|x\.com) ]]; then
    echo "ANTICRAWLER_TWITTER"
    return 0
  
  # Other known anti-crawler platforms
  elif [[ "$url" =~ (linkedin\.com|facebook\.com|instagram\.com) ]]; then
    echo "ANTICRAWLER_OTHER"
    return 1
  
  # Open web (could support direct addition)
  else
    echo "OPEN_WEB"
    return 2
  fi
}

Smart Extraction Workflow (Scene-Based)

Scenario 1: Twitter/X (Anti-Crawler)

# Direct path: Skip regular method, use browser extraction immediately
extract_twitter_content() {
  local url="$1"
  
  # Navigate
  skill_mcp playwright browser_navigate --arguments "{\"url\": \"$url\"}"
  
  # Wait and extract with multi-selector strategy
  skill_mcp playwright browser_run_code --arguments '{
    "code": "async (page) => {
      await page.waitForTimeout(6000);
      
      // Multi-selector priority
      const selectors = [
        '\''article [data-testid=\"tweetText\"]\'',  // Primary: tweet text
        '\''div[data-testid=\"tweet\"] span\'',          // Fallback 1
        '\''[role=\"article\"]\''                   // Fallback 2
      ];
      
      let content = null;
      for (const selector of selectors) {
        try {
          const element = await page.locator(selector).first();
          if (await element.count() > 0) {
            content = await element.textContent();
            if (content && content.trim().length > 0) {
              console.log(`Found with selector: ${selector}`);
              break;
            }
          }
        } catch (e) {
          // Try next selector
        }
      }
      
      // Ultimate fallback
      if (!content) {
        content = await page.evaluate(() => {
          const articles = document.querySelectorAll("article");
          return Array.from(articles).map(a => a.innerText).join("\\n---\\n");
        });
      }
      
      return {
        url: page.url(),
        title: await page.title(),
        content: content?.trim()
      };
    }"
  }'
  
  # Save to file and add to NotebookLM
  local filename="/tmp/twitter_extract_$(date +%s).md"
  echo "$EXTRACTED_CONTENT" > "$filename"
  notebooklm source add "$filename" --notebook "$NOTEBOOK_ID"
  
  echo "⚠️ Browser extraction used (anti-crawler protection)"
}

# Usage
extract_twitter_content "https://x.com/user/status/123"

Scenario 2: Open Web (Regular Method First)

# Try regular method first
try_regular_add() {
  local url="$1"
  local result=$(notebooklm source add "$url" --notebook "$NOTEBOOK_ID" --json)
  
  local status=$(echo "$result" | jq -r '.source.status // empty')
  
  case "$status" in
    READY|PROCESSING)
      echo "✓ Regular method succeeded"
      return 0
      ;;
    FAILED|AUTH_REQUIRED)
      echo "⚠️ Failed - trying browser extraction"
      # Fall back to browser extraction
      extract_content_via_browser "$url"
      ;;
    *)
      echo "❓ Unknown status, trying browser"
      extract_content_via_browser "$url"
      ;;
  esac
}

Scenario 3: Smart Batch Processing

# Process multiple URLs with intelligent method selection per URL
process_urls_intelligent() {
  local urls=("$@")
  local notebook_id="$1"
  
  for url in "${urls[@]}"; do
    local platform=$(detect_platform_type "$url")
    
    case "$platform" in
      ANTICRAWLER_TWITTER|ANTICRAWLER_OTHER)
        # Direct to browser (skip regular method)
        extract_content_via_browser "$url"
        ;;
      
      OPEN_WEB)
        # Try regular first
        try_regular_add "$url"
        ;;
    esac
  done
}

Research to Podcast (Interactive)

Time: 5-10 minutes total

1. notebooklm create "Research: [topic]" — if fails: check auth with notebooklm login
2. notebooklm source add for each URL/document — if one fails: log warning, continue with others
3. Wait for sources: notebooklm source list --json until all status=READY — required before generation
4. notebooklm generate audio "Focus on [specific angle]" (confirm when asked) — if rate limited: wait 5 min, retry once
5. Note the artifact ID returned
6. Check notebooklm artifact list later for status
7. notebooklm download audio ./podcast.mp3 when complete (confirm when asked)

Research to Podcast (Automated with Subagent)

Time: 5-10 minutes, but continues in background

When user wants full automation (generate and download when ready):

1. Create notebook and add sources as usual
2. Wait for sources to be ready (use source wait or check source list --json)
3. Run notebooklm generate audio "..." --json → parse artifact_id from output
4. Spawn a background agent using Task tool:
   Copy

   Task(
     prompt="Wait for artifact {artifact_id} in notebook {notebook_id} to complete, then download.
             Use: notebooklm artifact wait {artifact_id} -n {notebook_id} --timeout 600
             Then: notebooklm download audio ./podcast.mp3 -a {artifact_id} -n {notebook_id}",
     subagent_type="general-purpose"
   )
   

5. Main conversation continues while agent waits

Error handling in subagent:

• If artifact wait returns exit code 2 (timeout): Report timeout, suggest checking artifact list
• If download fails: Check if artifact status is COMPLETED first

Benefits: Non-blocking, user can do other work, automatic download on completion

Document Analysis

Time: 1-2 minutes

1. notebooklm create "Analysis: [project]"
2. notebooklm source add ./doc.pdf (or URLs)
3. notebooklm ask "Summarize the key points"
4. notebooklm ask "What are the main arguments?"
5. Continue chatting as needed

Bulk Import with Progressive Fallback

Time: Varies by source count

1. notebooklm create "Collection: [name]"
2. Add multiple sources (progressive fallback applied automatically):
   bashCopy

    # Always try direct addition first
    notebooklm source add "https://open-site.com/article" --notebook <id> --json
    # Monitor status: if FAILED, then use browser extraction
   
    # For anti-crawler URLs (fallback applied only if direct fails):
    # Twitter/X example
    notebooklm source add "https://x.com/user/status/123" --notebook <id> --json
    # If returns FAILED:
    skill_mcp playwright browser_navigate --arguments '{"url": "https://x.com/user/status/123"}'
    skill_mcp playwright browser_run_code --arguments '{...extract code...}'
    echo "# Extracted content" > /tmp/twitter_content.md
    notebooklm source add /tmp/twitter_content.md --notebook <id>
   

3. notebooklm source list to verify all sources status

Source limits: Max 50 sources per notebook Supported types: PDFs, YouTube URLs, web URLs, Google Docs, text files

Bulk Import with Source Waiting (Subagent Pattern)

Time: Varies by source count

When adding multiple sources and needing to wait for processing before chat/generation:

1. Add sources with --json to capture IDs:
   bashCopy

   notebooklm source add "https://url1.com" --json  # → {"source_id": "abc..."}
   notebooklm source add "https://url2.com" --json  # → {"source_id": "def..."}
   

2. Spawn a background agent to wait for all sources:
   Copy

   Task(
     prompt="Wait for sources {source_ids} in notebook {notebook_id} to be ready.
             For each: notebooklm source wait {id} -n {notebook_id} --timeout 120
             Report when all ready or if any fail.",
     subagent_type="general-purpose"
   )
   

3. Main conversation continues while agent waits
4. Once sources are ready, proceed with chat or generation

Why wait for sources? Sources must be indexed before chat or generation. Takes 10-60 seconds per source.

Deep Web Research (Subagent Pattern)

Time: 2-5 minutes, runs in background

Mixed Sources with Browser Extraction

Time: 5-15 minutes

When working with mixed sources (some direct, some anti-crawler):

1. Create notebook: notebooklm create "Mixed Collection"
2. Add direct sources normally:
   bashCopy

   notebooklm source add "https://open-site.com/article"
   notebooklm source add ./local-document.pdf
   

3. Handle protected sources with browser extraction:
   bashCopy

   # Twitter/X
   skill_mcp playwright browser_navigate --arguments '{"url": "https://x.com/user/status/123"}'
   skill_mcp playwright browser_run_code --arguments '{"code": "async (page) => { \
     await page.waitForTimeout(5000); \
     return await page.evaluate(() => document.querySelector('article')?.innerText); \
   }"}'
   # Save output to /tmp/twitter_extracted.md
   
   # LinkedIn
   skill_mcp playwright browser_navigate --arguments '{"url": "https://linkedin.com/post/..."}'
   skill_mcp playwright browser_run_code --arguments '{...code for LinkedIn...}'
   # Save output to /tmp/linkedin_extracted.md
   
   # Add extracted files
   notebooklm source add /tmp/twitter_extracted.md --notebook <id>
   notebooklm source add /tmp/linkedin_extracted.md --notebook <id>
   

4. Wait for all sources: notebooklm source list --json until all READY
5. Generate content as usual

Benefits:

• Seamlessly handle both open and protected platforms
• All content indexed in one notebook
• Browser extraction runs transparently to user

Deep research finds and analyzes web sources on a topic:

1. Create notebook: notebooklm create "Research: [topic]"
2. Start deep research (non-blocking):
   bashCopy

   notebooklm source add-research "topic query" --mode deep --no-wait
   

3. Spawn a background agent to wait and import:
   Copy

   Task(
     prompt="Wait for research in notebook {notebook_id} to complete and import sources.
             Use: notebooklm research wait -n {notebook_id} --import-all --timeout 300
             Report how many sources were imported.",
     subagent_type="general-purpose"
   )
   

4. Main conversation continues while agent waits
5. When agent completes, sources are imported automatically

Alternative (blocking): For simple cases, omit --no-wait:

notebooklm source add-research "topic" --mode deep --import-all
# Blocks for up to 5 minutes

When to use each mode:

• --mode fast: Specific topic, quick overview needed (5-10 sources, seconds)
• --mode deep: Broad topic, comprehensive analysis needed (20+ sources, 2-5 min)

Research sources:

• --from web: Search the web (default)
• --from drive: Search Google Drive

Output Style

Progress updates: Brief status for each step

• "Creating notebook 'Research: AI'..."
• "Adding source: https://example.com..."
• "Starting audio generation... (task ID: abc123)"

Fire-and-forget for long operations:

• Start generation, return artifact ID immediately
• Do NOT poll or wait in main conversation - generation takes 5-45 minutes (see timing table)
• User checks status manually, OR use subagent with artifact wait

JSON output: Use --json flag for machine-readable output:

notebooklm list --json
notebooklm source list --json
notebooklm artifact list --json

JSON schemas (key fields):

notebooklm list --json:

{"notebooks": [{"id": "...", "title": "...", "created_at": "..."}]}

notebooklm source list --json:

{"sources": [{"id": "...", "title": "...", "status": "READY|PROCESSING|FAILED"}]}

notebooklm artifact list --json:

{"artifacts": [{"id": "...", "title": "...", "type": "AUDIO_OVERVIEW", "status": "COMPLETED|PENDING|FAILED"}]}

Status values:

• Sources: PROCESSING → READY (or FAILED)
• Artifacts: PENDING → COMPLETED (or FAILED)

Error Handling

Progressive Fallback Strategy (Applied Automatically)

When regular source addition fails:

Success cases:

Browser-based extraction handling (fallback only): When encountering protected platforms (Twitter/X, LinkedIn, etc.):

1. Detection: Identify URL patterns: twitter.com, x.com, linkedin.com, facebook.com
2. Fallback method: Use Playwright browser automation instead of direct URL addition
3. Content extraction: Use JavaScript selectors or page.evaluate() to extract text
4. Save to file: Write extracted content to /tmp/ before adding to NotebookLM
5. NotebookLM integration: Add saved file as a source

Selector patterns for common platforms:

On failure, offer the user a choice:

1. Retry the operation
2. Skip and continue with something else
3. Investigate the error

Error decision tree:

Exit Codes

All commands use consistent exit codes:

Examples:

• source wait returns 1 if source not found or processing failed
• artifact wait returns 2 if timeout reached before completion
• generate returns 1 if rate limited (check stderr for details)

Known Limitations

Rate limiting: Audio, video, quiz, flashcards, infographic, and slides generation may fail due to Google's rate limits. This is an API limitation, not a bug.

Reliable operations: These always work:

• Notebooks (list, create, delete, rename)
• Sources (add, list, delete)
• Chat/queries
• Mind-map, study-guide, FAQ, data-table generation

Unreliable operations: These may fail with rate limiting:

• Audio (podcast) generation
• Video generation
• Quiz and flashcard generation
• Infographic and slides generation

Workaround: If generation fails:

1. Check status: notebooklm artifact list
2. Retry after 5-10 minutes
3. Use the NotebookLM web UI as fallback

Processing times vary significantly. Use the subagent pattern for long operations:

Polling intervals: When checking status manually, poll every 15-30 seconds to avoid excessive API calls.

Troubleshooting

notebooklm --help              # Main commands
notebooklm notebook --help     # Notebook management
notebooklm source --help       # Source management
notebooklm research --help     # Research status/wait
notebooklm generate --help     # Content generation
notebooklm artifact --help     # Artifact management
notebooklm download --help     # Download content

Re-authenticate: notebooklm login Check version: notebooklm --version Update skill: notebooklm skill install