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.
npx skills add roomi-fields/notebooklm-mcp --skill notebooklmgit clone --depth 1 https://github.com/roomi-fields/notebooklm-mcpWrote 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/skills/roomi-fields/notebooklm-mcp/notebooklm)<a href="https://agentmods.dev/skills/roomi-fields/notebooklm-mcp/notebooklm"><img src="https://agentmods.dev/badge/skills/roomi-fields/notebooklm-mcp/notebooklm/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/skills/roomi-fields/notebooklm-mcp/notebooklm"><img src="https://agentmods.dev/badge/skills/roomi-fields/notebooklm-mcp/notebooklm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00118 | $0.01067 |
| Opus 5.5 | $0.00047 | $0.00427 |
| Sonnet 5.5 | $0.00024 | $0.00213 |
| Haiku 4.5 | $0.00012 | $0.00107 |
Grade A, and why
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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NotebookLM
Overview
NotebookLM answers questions only from the sources uploaded to a notebook,
with inline citations to the exact passages used — no open-web knowledge, so
answers are hallucination-resistant and fully traceable. This skill drives the
@roomi-fields/notebooklm-mcp engine to query notebooks, manage sources, and
generate Studio content, and encodes the patterns that make NotebookLM usable at
research scale (citation formats, the ~50-queries/day quota, batch-to-cache).
Choosing the transport
Two ways reach the same engine — pick per what the session already has:
- notebooklm MCP tools — if tools such as
notebook_ask/source_add/server_health(ormcp__notebooklm__*) are available in the session, call them directly. This is the preferred path and needs no server. - HTTP REST API — otherwise, use the bundled
scripts/nblm.sh, which talks to a running NotebookLM MCP server (defaulthttp://localhost:3000, override withNOTEBOOKLM_SERVER_URL). If no server is reachable, ask the user to start one (npm run start:httpfrom a clone) or to install the MCP.
Both are backed by the same account and session, so the choice is purely about which is already wired up.
Prerequisite: one Google login
NotebookLM needs a signed-in Google session (saved once, reused across runs).
Verify with nblm.sh health (or the server_health tool) — look for
authenticated: true. If not authenticated, run the interactive login in a
terminal (a visible Chrome window opens):
notebooklm-mcp-setup-auth # global install
# or: scripts/nblm.sh auth
Run the login in a terminal rather than through an in-client tool: interactive Google login can take minutes and a stdio client's tool-call timeout may cut it off.
Core tasks
Use scripts/nblm.sh for the REST path (or the equivalent MCP tool):
scripts/nblm.sh health # reachability + auth status
scripts/nblm.sh notebooks # list notebooks (id + name)
scripts/nblm.sh ask "<question>" <notebook_id> # citation-backed answer (JSON citations)
scripts/nblm.sh generate <notebook_id> report # audio|report|video|infographic|presentation|data_table|flashcards|quiz|mind_map
What ships with it
3 files 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.
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.
- 1mo ago First seen · 91 lines · 118 tokens per session scan A 8921fc61ca50
notebooklm is a skill published in the GitHub repository roomi-fields/notebooklm-mcp (204 stars, last pushed today), licensed MIT. It adds 118 tokens to every session and 1,067 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
create-skill
Create instruction-only skills that package knowledge and workflow guides for AI clients.
frontmcp-guides
Tutorials, end-to-end walkthroughs, and complete reference projects for FrontMCP. Use when you want a getting-started guide, a full worked example, or to learn best practices by following a step-by-step build rather than a single API reference. Includes a beginner weather-API server (tool plus static resource, Zod…
class-management
Use when the user wants to create a new class, group, or course in Zooza — e.g. "set up a Monday yoga class", "add a new group", "schedule a course". Guides through programme selection, venue, trainer, session pattern, and commits.
n8n-agents
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain. AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output…
n8n-error-handling
Wire n8n error handling so failures are loud, structured, and recoverable. Use when building any webhook/API workflow, a scheduled or unattended workflow, or any path where a silent failure would drop user-visible work — and whenever the user mentions error handling, onError, continueErrorOutput, error…
n8n-subworkflows
Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over 10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular workflows, "Define Below" inputs…