/notebooklm

name: notebooklm
description: This skill should be used when the user wants to query their Google NotebookLM notebooks for citation-backed, source-grounded answers, or manage notebooks, sources, and Studio content (audio, report, video, infographic, presentation, data table, flashcards, quiz, mind map). It drives the @roomi-fields/notebooklm-mcp engine — via the notebooklm MCP tools when they are available in the session, otherwise via its HTTP REST API — and covers Google login, citation formats, the daily-quota-aware batch/ingestion pattern, and source discovery.

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:

1. **notebooklm MCP tools** — if tools such as `notebook_ask` / `source_add` / `server_health` (or `mcp__notebooklm__*`) are available in the session, call them directly. This is the preferred path and needs no server. 2. **HTTP REST API** — otherwise, use the bundled `scripts/nblm.sh`, which talks to a running NotebookLM MCP server (default `http://localhost:3000`, override with `NOTEBOOKLM_SERVER_URL`). If no server is reachable, ask the user to start one (`npm run start:http` from 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

• **Ask**: the script requests `source_format: json`, so the answer carries

source names + cited excerpts. For a human-facing answer, prefer `expanded` (see `references/rest-api.md` to vary the format).

• **Generate**: `flashcards`/`quiz` route to the study-aid endpoint and

`mind_map` to the mind-map endpoint automatically.

Working effectively (read before large runs)

For anything beyond a few questions, load `references/research-workflows.md`. Key points:

• **Quota**: free accounts cap at ~50 chat queries/day. Rotate accounts

(`/re-auth`) or, better, **ingest once and retrieve offline**.

• **Batch → cache**: for literature reviews / SOTA surveys, run an exhaustive

question set through `/batch-to-vault` (writes markdown + `nblm-answer-v1` JSON sidecars with citations), then answer repeated questions from the cache (e.g. with [RTFM](https://github.com/roomi-fields/rtfm)) — unlimited, offline.

• **Fresh vs. follow-up**: omit `session_id` for independent questions (fastest);

pass a stable one to continue a conversation.

References

• `references/rest-api.md` — endpoint + body reference for the HTTP path.
• `references/research-workflows.md` — citation formats, quota strategy, the

batch/ingestion pattern, source discovery.

Installing the engine

If neither the MCP tools nor a server are present, the engine is the npm package [`@roomi-fields/notebooklm-mcp`](https://github.com/roomi-fields/notebooklm-mcp) (also a Claude Code plugin via the `roomi-fields/claude-plugins` marketplace). Point the user there, then run the one-time login above.