Deep Research via NotebookLM

SkillDocs & knowledge

deep-research-notebooklm is a skill that lets an AI agent run structured research across multiple sources using Google NotebookLM as the research engine. It handles tasks like market analysis, competitive intel, trend analysis, and prospect research, then returns formatted briefs and optional studio artifacts such as slides, audio podcasts, or video.

Available today. Use it from your connected AI after setup.

Have access to Google NotebookLM, which the skill uses as its research engine.

Then ask your AI: use the Deep Research via NotebookLM skill

What your AI can do with it

  • Conducts structured multi-source research through Google NotebookLM
  • Runs market analysis and competitive intel workflows
  • Performs trend analysis and prospect research
  • Delivers formatted research briefs
  • Generates studio artifacts including slides, audio podcasts, and video

Getting started

  1. Have access to Google NotebookLM, which the skill uses as its research engine.
  2. Add the deep-research-notebooklm skill to your agent's available skills.
  3. Ask your agent to run a research task, such as a market analysis or prospect brief.
  4. Review the formatted brief the agent returns, and request studio artifacts like slides or an audio podcast if needed.

What this skill tells your AI

The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/ai-research/deep-research-notebooklm/SKILL.md and read by ahel’s review.

Research $ARGUMENTS deeply using the NotebookLM MCP server and deliver a structured research brief. Optionally generate studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps) from the research.

Prerequisites

  • NotebookLM MCP server must be configured. Install via: nlm setup add claude-code
  • If NotebookLM MCP tools are not available, tell the user to run the setup command and restart their session.

Research Workflow

Step 1: Define Scope

Determine the research type based on the user's request:

TypeFocus
Market ResearchIndustry trends, market sizing, opportunities, TAM/SAM/SOM
Competitive IntelCompetitor analysis, positioning gaps, feature comparisons
Client/Prospect ResearchCompany background, pain points, decision makers, recent news
Trend AnalysisTechnology trends, adoption patterns, forecasts, emerging players
Proposal ResearchBackground for proposals, sector-specific data, case studies
Academic/TechnicalPapers, frameworks, methodologies, state of the art

Tell the user what you plan to research and confirm the angle:

"I'll research [topic]. My angle: [specific focus]. I'll investigate: [2-3 specific questions]. Sound right, or should I adjust?"

Wait for confirmation before proceeding.

Step 2: Create NotebookLM Notebook

Use notebook_create to create a notebook named: Research: [Topic] - [YYYY-MM-DD]

Step 3: Add Context Sources

Use source_add to seed the notebook with relevant context:

  • Add any URLs the user provides (articles, company pages, reports)
  • Add any documents or files the user references
  • Add text summaries of relevant background if no URLs are available
  • If researching a company, add their website, LinkedIn, recent press

Step 4: Run Research

Use research_start with a well-crafted query based on the topic and context.

Mode selection:

  • Default: "fast" (~60 seconds, ~10 sources) -- good for most queries
  • Use "deep" only if the user explicitly asks for exhaustive research (can take 10+ minutes and may stall at 0 sources)

Tip: Run direct WebSearch calls in parallel with NotebookLM for faster initial data gathering while the research engine works.

Poll research_status until complete. Use the query parameter as fallback matching -- task IDs can change between research_start and research_status calls.

Step 5: Import Discovered Sources

Use research_import to bring discovered sources into the notebook for deeper analysis.

Step 6: Query for Insights

Use notebook_query to ask 3-5 targeted questions based on the research type:

  1. Overview: "What are the key findings about [topic]?"
  2. Opportunities: "What opportunities or gaps exist in this space?"
  3. Actions: "What are the most actionable insights from this research?"
  4. Risks: "What are the main risks, challenges, or counterarguments?"
  5. Custom: A question specific to the research type (e.g., "Who are the top 5 competitors and how do they differentiate?" for competitive intel)

Step 7: Write Research Brief

Save the findings to a local file using the research brief template:

File path: research/[topic-slug]-[YYYY-MM-DD].md

Use the template from research-brief-template.md to structure the output. Create the research/ directory if it does not exist.

Step 8: Present Takeaways

After saving, present the user with:

  • 3-5 headline findings (bullets, direct, no filler)
  • 1-2 recommended actions connected to the user's stated goals
  • Surprises or contrarian findings -- anything that challenges assumptions
  • The file path where the full brief is saved
  • The NotebookLM notebook URL so the user can explore sources directly

Step 9 (Optional): Generate Studio Artifacts

Ask the user: "Want me to generate any artifacts from this research? Options: slides, audio (podcast), video, infographic, report, mind map."

If yes, use studio_create with the notebook_id from Step 2.

Available artifact types and recommended settings:

TypeKey paramsBest for
slide_deckslide_format: detailed_deck or presenter_slides; slide_length: short or defaultExecutive presentations, client pitches
audioaudio_format: deep_dive, brief, critique, or debate; audio_length: short, default, longPodcast-style deep dives, learning on the go
videovideo_format: explainer, brief, cinematic; visual_style: auto_select, classic, whiteboard, etc.Visual explainers, social media content
infographicorientation: landscape, portrait, square; infographic_style: professional, bento_grid, etc.One-pagers, social sharing
reportreport_format: Briefing Doc, Study Guide, Blog Post, Create Your OwnWritten deliverables, summaries
mind_maptitleVisual knowledge mapping

Common params for all artifact types:

  • language: Set to the user's preferred language (e.g., "en", "es", "pt")
  • focus_prompt: A clear directive about what to emphasize in the artifact
  • confirm: Must be true to proceed with generation

After creating an artifact:

  1. Poll studio_status until completed (audio/video: 5-15 min; slides/infographics: 2-5 min)
  2. Use download_artifact to save locally if needed
  3. Provide the notebook URL so the user can access artifacts directly

Tips:

  • audio with deep_dive format produces the best podcast-style analysis
  • slide_deck with detailed_deck format works best for standalone reading; presenter_slides is better when accompanied by speaker notes
  • Audio status may show "unknown" once completed -- check for audio_url presence instead of waiting for a "completed" status

Notes

  • Fast mode is recommended as the default. Deep mode is powerful but can take 10+ minutes and occasionally stalls.
  • Always confirm the research scope with the user before starting -- a well-scoped query produces dramatically better results.
  • The research brief template ensures consistent, actionable output across all research types.

Additional Resources

Signals

GitHub stars
32k
Forks
4k
Last commit
Sep 2026

Questions

What research tasks does it support?
It supports structured multi-source research such as market analysis, competitive intel, trend analysis, and prospect research.
What does it deliver?
Formatted research briefs, plus optional studio artifacts including slides, audio podcasts, and video.
What powers the research?
Google NotebookLM serves as the research engine behind the skill.
Advanced
Item type
skill
Key
deep-research-notebooklm
Source
github.com/davila7/claude-code-templates