deep-research-notebooklm

deep-research-notebooklm is a skill for Claude Code from davila7/claude-code-templates. It costs 68 tokens per session (1,575 once invoked), scanned A, original, MIT.

A structured research workflow that uses NotebookLM, a Google tool for working with source documents, to study several sources and produce a research brief. It can also create optional formats such as slides, audio, video, infographics, reports, and mind maps.

In plain words
What is it for?
Use it for market research, competitor analysis, trend analysis, prospect research, proposals, or academic and technical topics. It helps turn gathered sources into a brief and optional presentation or media outputs.
Why use it?
It organizes source-based research into a defined process instead of leaving findings scattered across documents. It requires the NotebookLM connection to be configured first.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

Good fit Use it for market research, competitor analysis, trend analysis, prospect research, proposals, or academic and technical topics. It helps turn gathered sources into a brief and optional presentation or media outputs.

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Install with agentmods
npx agentmods add skills/davila7/claude-code-templates/deep-research-notebooklm
About the project

Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.

davila7/claude-code-templates · 30,559 stars · on GitHub · aitmpl.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add davila7/claude-code-templates --skill deep-research-notebooklm
Clone the repo
git clone --depth 1 https://github.com/davila7/claude-code-templates

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deep-research-notebooklm

README.md
[![agentmods](https://agentmods.dev/badge/skills/davila7/claude-code-templates/deep-research-notebooklm.svg)](https://agentmods.dev/skills/davila7/claude-code-templates/deep-research-notebooklm)
Your own site
<a href="https://agentmods.dev/skills/davila7/claude-code-templates/deep-research-notebooklm"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/deep-research-notebooklm.svg" alt="Measured on agentmods" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,575 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.01575
Opus 5 $0.00034 $0.00788
Sonnet 5 $0.00014 $0.00315
Haiku 4.5 $0.00007 $0.00158

Measured 9d ago against content hash fd3a737335b5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

deep-research-notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

cli-tool/components/skills/ai-research/deep-research-notebooklm/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on 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)

Read the full file on GitHub · 133 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 133 lines · 68 tokens per session scan A fd3a737335b5

Subscribe to this mod's changes

deep-research-notebooklm is a skill published in the GitHub repository davila7/claude-code-templates (30,559 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,575 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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