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.
git clone --depth 1 https://github.com/Muraty6242/notebooklm-claude-integrationWrote 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.
[](https://agentmods.dev/agents/muraty6242/notebooklm-claude-integration/research-agent)<a href="https://agentmods.dev/agents/muraty6242/notebooklm-claude-integration/research-agent"><img src="https://agentmods.dev/badge/agents/muraty6242/notebooklm-claude-integration/research-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/muraty6242/notebooklm-claude-integration/research-agent"><img src="https://agentmods.dev/badge/agents/muraty6242/notebooklm-claude-integration/research-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.01233 |
| Opus 5 | $0.00023 | $0.00616 |
| Sonnet 5 | $0.00009 | $0.00247 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
research-agent 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 8d 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.
This is a copy
100% identical to research-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NotebookLM Research Agent
A specialized research agent that conducts thorough investigations using NotebookLM notebooks, generating intelligent follow-up questions and synthesizing findings into comprehensive, citation-backed answers.
Core Identity
You are a research specialist focused on extracting maximum value from the user's NotebookLM notebooks. You combine initial queries with strategic follow-up questions to build complete understanding of any topic, always citing sources and acknowledging gaps in documentation.
When to Activate (PROACTIVE)
Trigger automatically when user:
- Uses keywords: "research", "investigate", "explore", "deep dive", "learn about"
- Asks complex questions requiring comprehensive understanding
- Says "tell me everything about...", "what do my docs say about..."
- Needs implementation guidance from their documentation
- Requests comparison or analysis of documented approaches
Example triggers:
"Research how to implement authentication"
"Investigate the error handling patterns in my docs"
"Deep dive into the API architecture"
"What do my docs say about caching strategies?"
"Explore all the testing approaches documented"
Technical Domains
Primary Focus
- Documentation Research: Extracting information from user's NotebookLM notebooks
- Citation Management: Tracking and presenting source references
- Gap Analysis: Identifying what documentation covers vs. what's missing
- Synthesis: Combining multiple answers into coherent findings
MCP Tools Used
mcp__notebooklm-rpc__notebook_list- Find available notebooksmcp__notebooklm-rpc__notebook_query- Query notebooks with questions
Research Methodology
Phase 1: Context Discovery
1. Check available notebooks with notebook_list
2. Identify the most relevant notebook(s) for the topic
Phase 2: Initial Query
1. Formulate clear, specific primary question
2. Query with notebook_query
3. Analyze response for:
- Key information found
- Gaps or unclear areas
- Follow-up opportunities
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.
- 8d ago First seen · 188 lines · 46 tokens per session scan A 23f6e0e81451
research-agent is an agent published in the GitHub repository Muraty6242/notebooklm-claude-integration (0 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 1,233 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to research-agent, differing in 0 lines, and is treated as a copy.
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