NotebookLM Research Workflow
claude-world/notebooklm-skill
Creates NotebookLM notebooks from URLs, text and files, asks cited questions, runs web research and generates audio, slides, quizzes and other artifacts.
Deep research skill powered by NotebookLM MCP. An agent skill from davila7/claude-code-templates.
Install Deep Research Notebooklm
$ npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install davila7/claude-code-templates deep-research-notebooklm --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/ai-research/deep-research-notebooklm .claude/skills/deep-research-notebooklm && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deep-research-notebooklm" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklm into .claude/skills/deep-research-notebooklm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-notebooklm", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklmType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install davila7/claude-code-templates deep-research-notebooklm --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .agents/skills && cp -r skills-src/cli-tool/components/skills/ai-research/deep-research-notebooklm .agents/skills/deep-research-notebooklm && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deep-research-notebooklm" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklm into .agents/skills/deep-research-notebooklm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-notebooklm", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install davila7/claude-code-templates deep-research-notebooklm --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/cli-tool/components/skills/ai-research/deep-research-notebooklm .cursor/skills/deep-research-notebooklm && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deep-research-notebooklm" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklm into .cursor/skills/deep-research-notebooklm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-notebooklm", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/davila7/claude-code-templates.git --path cli-tool/components/skills/ai-research/deep-research-notebooklm--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install davila7/claude-code-templates deep-research-notebooklm --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/cli-tool/components/skills/ai-research/deep-research-notebooklm .gemini/skills/deep-research-notebooklm && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deep-research-notebooklm" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklm into .gemini/skills/deep-research-notebooklm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-notebooklm", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install davila7/claude-code-templates deep-research-notebooklmInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .github/skills && cp -r skills-src/cli-tool/components/skills/ai-research/deep-research-notebooklm .github/skills/deep-research-notebooklm && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deep-research-notebooklm" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklm into .github/skills/deep-research-notebooklm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-notebooklm", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install davila7/claude-code-templates deep-research-notebooklm --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/cli-tool/components/skills/ai-research/deep-research-notebooklm .opencode/skills/deep-research-notebooklm && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deep-research-notebooklm" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/deep-research-notebooklm into .opencode/skills/deep-research-notebooklm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-notebooklm", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
deep-research-notebooklm- GitHub stars
- 33k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 814 words
- Files
- 2
- Skills in repo
- 479
- Repo updated
- First seen
- Licence
- MIT
At a glance
Deep research skill powered by NotebookLM MCP. An agent skill from davila7/claude-code-templates.
- Works in 9 steps: Define Scope → Create NotebookLM Notebook → Add Context Sources → …
- Tasks that involve Deep research
- SKILL.md covers Prerequisites, Research Workflow, Notes and Additional Resources
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Deep Research Notebooklm is an agent skill from davila7/claude-code-templates. 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).
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `research-brief-template.md`).
It sits in Research & Science, covering Deep research and Source-grounded notebooks. It works with NotebookLM and Model Context Protocol. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.
When your agent uses it
- Tasks that involve Deep research
- Tasks that involve Source-grounded notebooks
Example prompts
- “/deep-research-notebooklm”
Workflow steps
9 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit c0ca7da. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Deep Research Notebooklm loads about 1.7k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 814 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from davila7/claude-code-templates at commit c0ca7da, republished under its MIT licence (© davila7). 814 words, ~1,706 tokens.
Download SKILL.mdSave it as
.claude/skills/deep-research-notebooklm/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- deep-research-notebooklm
- description
- 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).
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:
- Overview: "What are the key findings about [topic]?"
- Opportunities: "What opportunities or gaps exist in this space?"
- Actions: "What are the most actionable insights from this research?"
- Risks: "What are the main risks, challenges, or counterarguments?"
- 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
Show full SKILL.md (299 more words)Show less
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 artifactconfirm: Must betrueto proceed with generation
After creating an artifact:
- Poll
studio_statusuntilcompleted(audio/video: 5-15 min; slides/infographics: 2-5 min) - Use
download_artifactto save locally if needed - Provide the notebook URL so the user can access artifacts directly
Tips:
audiowithdeep_diveformat produces the best podcast-style analysisslide_deckwithdetailed_deckformat works best for standalone reading;presenter_slidesis better when accompanied by speaker notes- Audio status may show
"unknown"once completed -- check foraudio_urlpresence 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
- research-brief-template.md -- Template for structuring research brief output
© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in cli-tool/components/skills/ai-research/deep-research-notebooklm of davila7/claude-code-templates.
- SKILL.md
- research-brief-template.md
Open the folder on GitHubat commit c0ca7da
Compare with similar skills
Deep Research Notebooklm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deep Research Notebooklm this skilldavila7/claude-code-templates | 33k | — | ~1.7k | Automated safety check: Pass | MIT | |
| NotebookLM Research Workflowclaude-world/notebooklm-skill | 467 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Researchiusztinpaul/ai-research-os-workshop | 179 | — | ~17k | Automated safety check: Pass | MIT | |
| Multi-Source to NotebookLM Processorjoeseesun/qiaomu-anything-to-notebooklm | 6.2k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Notebooklmroomi-fields/notebooklm-mcp | 192 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Nlm Skilliusztinpaul/ai-research-os-workshop | 179 | 1 repos | ~6.9k | Automated safety check: Pass | MIT |
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Works with
Categories
Questions about Deep Research Notebooklm
What does Deep Research Notebooklm do?
Deep research skill powered by NotebookLM MCP. An agent skill from davila7/claude-code-templates. Deep Research Notebooklm is an agent skill from davila7/claude-code-templates. Deep research skill powered by NotebookLM MCP.
When should I use Deep Research Notebooklm?
Deep Research Notebooklm fits situations like: tasks that involve Deep research; tasks that involve Source-grounded notebooks.
How do I install Deep Research Notebooklm in Claude Code?
Run `npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a claude-code`. Or copy the skill folder (cli-tool/components/skills/ai-research/deep-research-notebooklm in davila7/claude-code-templates) into .claude/skills/deep-research-notebooklm in your project. Claude Code loads it when a task matches its description.
How do I install Deep Research Notebooklm in Codex?
Run `npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a codex`. Or copy the skill folder (cli-tool/components/skills/ai-research/deep-research-notebooklm in davila7/claude-code-templates) into .agents/skills/deep-research-notebooklm in your project. Codex loads it when a task matches its description.
Can I use Deep Research Notebooklm in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill deep-research-notebooklm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-research-notebooklm, .gemini/skills/deep-research-notebooklm, .github/skills/deep-research-notebooklm and .opencode/skills/deep-research-notebooklm in your project.
What does Deep Research Notebooklm need to run?
SKILL.md names no scripts, command-line tools or credentials: Deep Research Notebooklm is instructions for the agent only.
Does Deep Research Notebooklm access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Deep Research Notebooklm safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Deep Research Notebooklm use?
Deep Research Notebooklm is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Deep Research Notebooklm use?
About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Deep Research Notebooklm?
Skills that share tags, products or a category with Deep Research Notebooklm: NotebookLM Research Workflow (claude-world/notebooklm-skill, 467 stars), Research (iusztinpaul/ai-research-os-workshop, 179 stars), Multi-Source to NotebookLM Processor (joeseesun/qiaomu-anything-to-notebooklm, 6.2k stars) and Notebooklm (roomi-fields/notebooklm-mcp, 192 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Deep Research Notebooklm?
davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,512 GitHub stars. The repository holds 479 skills in this directory. The repository was last updated on October 10, 2026.
Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.