deep-research

name	deep-research
description	Multi-source deep research combining YouTube, web search, and article URLs into NotebookLM as a free RAG backend. Use when user wants to research a topic in depth, find up-to-date information, generate analysis, infographics, podcasts, quizzes, or any NotebookLM artifact. Triggers on "research X", "deep dive into X", "find everything about X", or /deep-research.
user_invocable	true
arguments	topic to research

Deep Research Skill

Research any topic using multiple sources (YouTube, web articles, web research) fed into NotebookLM as a free RAG analysis engine. Zero token cost for the research itself — only tokens used are for sending/receiving commands.

Why This Works

• YouTube = expert video content, tutorials, talks, interviews
• Web research = NotebookLM's built-in web search finds and indexes articles automatically
• WebSearch = Claude's web search finds specific articles, blog posts, papers to add as URL sources
• NotebookLM = free Google-powered RAG that indexes all sources and generates artifacts
• Claude Code = orchestrator that runs all source-gathering in parallel
• Result: comprehensive, multi-perspective research without burning Claude tokens on analysis

Workflow

User query
  ├── YouTube search (yt-search) ──────────────┐
  ├── Web research (notebooklm add-research) ──┼── NotebookLM notebook ── Analyze/Generate
  └── Article search (WebSearch + add URLs) ───┘

Step-by-Step Execution

Step 1: Create NotebookLM Notebook

Create the notebook first so all source-adding steps can target it.

notebooklm create "<TOPIC> Research" --json
# Parse the notebook ID from output
notebooklm use <notebook_id>

Step 2: Gather Sources (run in parallel)

Launch three parallel tracks to maximize source diversity:

Track A: YouTube Videos

Use /yt-search to find relevant videos:

PYTHONIOENCODING=utf-8 python -m yt_dlp "ytsearch10:<TOPIC>" --flat-playlist -j 2>/dev/null

• Pick top 5-10 by view count and relevance
• Add each to the notebook:

  notebooklm source add "https://www.youtube.com/watch?v=VIDEO_ID" --notebook <notebook_id>
  

Track B: NotebookLM Web Research (automatic)

Let NotebookLM search the web and import sources itself:

# Fast mode: 5-10 sources, ~30 seconds
notebooklm source add-research "<TOPIC>" --notebook <notebook_id>

# Deep mode: 20+ sources, 2-5 minutes (use for thorough research)
notebooklm source add-research "<TOPIC>" --mode deep --no-wait --notebook <notebook_id>

• Fast mode is good for focused topics
• Deep mode is better for broad or complex topics
• Deep mode runs async — use notebooklm research wait --notebook <notebook_id> --import-all to wait

Track C: Targeted Article URLs (optional but powerful)

Use Claude's WebSearch to find high-quality articles, then add them:

# Use WebSearch tool to find articles, blog posts, papers
# Then add each relevant URL:
notebooklm source add "https://example.com/article" --notebook <notebook_id>
notebooklm source add "https://blog.example.com/post" --notebook <notebook_id>

Good for:

• Industry reports and whitepapers
• Blog posts from known experts
• Documentation and official guides
• Academic papers and case studies

Step 3: Wait for Sources

Ensure all sources are indexed before analysis:

notebooklm source list --notebook <notebook_id> --json
# Check all sources have status "ready"
# Or wait for specific sources:
notebooklm source wait <source_id> -n <notebook_id>

For deep research, also wait for research completion:

notebooklm research wait -n <notebook_id> --import-all --timeout 300

Step 4: Analyze with NotebookLM

notebooklm ask "Analyze the key strategies, frameworks, and trends discussed across all sources. Provide a comprehensive summary." --notebook <notebook_id>

# Ask specific follow-up questions
notebooklm ask "<specific question>" --notebook <notebook_id>

Step 5: Generate Artifacts (Optional)

Artifact	Command	Output
Audio podcast	notebooklm generate audio	MP3
Video overview	notebooklm generate video	MP4
Infographic	notebooklm generate infographic	PNG
Slide deck	notebooklm generate slide-deck	PDF/PPTX
Quiz	notebooklm generate quiz	JSON/MD
Flashcards	notebooklm generate flashcards	JSON/MD
Report	notebooklm generate report --format briefing-doc	Markdown
Mind map	notebooklm generate mind-map	JSON
Data table	notebooklm generate data-table "description"	CSV

Download artifacts:

notebooklm download audio output.mp3
notebooklm download infographic output.png

Step 6: Return Results to User

• Present the analysis in a clean, structured format
• List source breakdown: X YouTube videos, Y web research sources, Z articles
• Offer to generate additional artifacts if useful

Parallel Execution Strategy

For maximum efficiency, use subagents:

Main agent: Create notebook
  ├── Subagent 1: yt-search → add YouTube videos → wait for indexing
  ├── Subagent 2: add-research (deep) → wait → import-all
  └── Subagent 3: WebSearch → add article URLs → wait for indexing
Main agent: Once all ready → analyze + generate artifacts

Use the --notebook <id> flag on all commands so subagents don't conflict on context.

Research Depth Levels

Level	YouTube	Web Research	Articles	Best For
Quick	5 videos	fast mode	skip	Quick overview, familiar topic
Standard	10 videos	fast mode	3-5 URLs	Most research tasks
Deep	15-20 videos	deep mode	5-10 URLs	Comprehensive analysis, new domain

Default to Standard. Use Deep when user says "thorough", "comprehensive", or "deep dive".

Tips

• Diversity wins — mixing YouTube + web articles + auto-research gives the richest analysis
• Filter YouTube by views — higher view count usually means better quality
• Use WebSearch strategically — search for "[topic] guide 2026" or "[topic] case study" to find written content YouTube doesn't cover
• Deep research is async — start it first, add YouTube/articles while it runs
• Source limits — Standard plan: 50 sources/notebook. Don't overshoot.
• Save results — notebooklm ask "..." --save-as-note preserves Q&A in the notebook

Example Prompt

/deep-research meta ad strategies 2026

This will:

1. Create a NotebookLM notebook
2. Search YouTube for top 10 videos on meta ad strategies
3. Run deep web research via NotebookLM
4. Find 5 top articles/guides via WebSearch
5. Wait for all sources to index
6. Generate comprehensive analysis
7. Optionally create infographic/slides/podcast

Credits

Workflow inspired by Erik Lazar. NotebookLM Python API by teng-lin/notebooklm-py.