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Deep Research Notebooklm skill

Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps).

$npx skills add davila7/claude-code-templates --skill deep-research-notebooklm
GitHub stars
33,273
Pack installs
285k
Alyn Score
78/99
Updated
7 h ago
License
MIT

Install Deep Research Notebooklm

Pick your agent. Every command is ready to paste.

$npx skills add davila7/claude-code-templates --skill deep-research-notebooklm

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads deep-research-notebooklm when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the deep-research-notebooklm skill”. In Codex, type $deep-research-notebooklm.

The skill itself

SKILL.md

GitHub

Deep Research via NotebookLM

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:

Type Focus
Market Research Industry trends, market sizing, opportunities, TAM/SAM/SOM
Competitive Intel Competitor analysis, positioning gaps, feature comparisons
Client/Prospect Research Company background, pain points, decision makers, recent news
Trend Analysis Technology trends, adoption patterns, forecasts, emerging players
Proposal Research Background for proposals, sector-specific data, case studies
Academic/Technical Papers, 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:

Type Key params Best for
slide_deck slide_format: detailed_deck or presenter_slides; slide_length: short or default Executive presentations, client pitches
audio audio_format: deep_dive, brief, critique, or debate; audio_length: short, default, long Podcast-style deep dives, learning on the go
video video_format: explainer, brief, cinematic; visual_style: auto_select, classic, whiteboard, etc. Visual explainers, social media content
infographic orientation: landscape, portrait, square; infographic_style: professional, bento_grid, etc. One-pagers, social sharing
report report_format: Briefing Doc, Study Guide, Blog Post, Create Your Own Written deliverables, summaries
mind_map title Visual 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

Tips for Deep Research Notebooklm

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • It is part of a pack with 40 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about Deep Research Notebooklm

What does the Deep Research Notebooklm skill do?

Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps).

How do I install Deep Research Notebooklm?

Run "npx skills add davila7/claude-code-templates --skill deep-research-notebooklm" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does Deep Research Notebooklm work in Cursor and Codex?

Yes. Deep Research Notebooklm uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is Deep Research Notebooklm free?

Deep Research Notebooklm is open source on GitHub under the MIT license. It is free to install; check the license before commercial use.

Who made Deep Research Notebooklm?

Deep Research Notebooklm is published by davila7 in the davila7/claude-code-templates repository, together with 39 other skills.

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