Notebooklm

description: Vault-first source-grounded research via Gemini File Search. One command, no browser. The grounded parallel to /research-deep (which is open-web via Perplexity).

category: research

triggers_en: ["notebooklm", "research grounded", "ground research in vault", "ask my notebook", "source-grounded research"]

Use the obsidian-second-brain skill. Execute /notebooklm [topic]:

1. Resolve the topic from the user's argument. If no topic, ask: "What topic for source-grounded research?"

1. Run the Python command from the repo root (~/Projects/personal/obsidian-second-brain/):

   uv run -m scripts.research.notebooklm --topic "<topic>"

1. The script does the whole flow end-to-end:

• Scans the vault for the top 12 relevant notes (same shape as /research-deep Phase 1).
• Uploads them to a fresh Gemini File Search store.
• Asks Gemini (default gemini-2.5-pro, override via NOTEBOOKLM_MODEL env) for a synthesis grounded against those sources.
• Writes the AI-first synthesis to Research/NotebookLM/YYYY-MM-DD - <slug>.md.
• Deletes the File Search store so nothing is left behind.
• Emits a <<<NOTEBOOKLM_PROPAGATION_PAYLOAD>>> JSON block.

1. After save, do the propagation step. Same flow as /research-deep:

• Parse the propagation payload.
• Read the saved synthesis at saved_note.
• Treat the synthesis as the "conversation context" input to /obsidian-save.
• Run the standard /obsidian-save flow: spawn parallel subagents (People, Projects, Tasks, Decisions, Ideas) and update vault notes per any "Recommended next reads or angles" bullets if they map to entities or projects.
• Link the new synthesis note from today's daily note.

1. Report back to the user: "Saved [[YYYY-MM-DD - <slug>]] to Research/NotebookLM/. Linked from today's daily note. Updated [[X]], created [[Y]]."

1. Plain English triggers: "notebooklm this", "ground research on X using my vault", "source-grounded research on X", "ask my own notes about X".

1. When to choose /notebooklm over /research-deep:

• /research-deep (Perplexity + Grok): when you want OPEN-WEB + X-discourse coverage. Cost: $0.20-0.80.
• /notebooklm (Gemini File Search): when you want answers GROUNDED IN your own vault. Cost: ~$0.01-0.05.
• Run both for high-value topics. The web view and the grounded view rarely contradict, and the contradictions are where the insight is.

1. Configuration: requires GEMINI_API_KEY in ~/.config/obsidian-second-brain/.env. Get one free at https://aistudio.google.com/apikey. Optional NOTEBOOKLM_MODEL override (default gemini-2.5-pro).

AI-first rule: Every note created or updated by this command MUST follow references/ai-first-rules.md. The saved synthesis at Research/NotebookLM/YYYY-MM-DD - <slug>.md follows the template baked into the script (preamble, frontmatter, vault-baseline links, response verbatim). Do not strip those.

Anti-fabrication: Search exhaustively before claiming any note, person, or file is absent - false absence is the most common failure mode - and never invent facts, entities, or dates (mark unknowns as TBD). See the anti-fabrication and search-completeness hard rules in references/ai-first-rules.md.

Why Gemini File Search and not the browser: NotebookLM has no public API for personal Google accounts. Gemini File Search (generally available, plain API key, same Gemini model family) gives the same architectural shape: source-grounded retrieval, multi-document context, citation-style synthesis. One HTTP call, no manual paste step.

Cost: $0.15 per million tokens indexed, storage free, generation at standard Gemini token rates. For a 12-note vault bundle (~30K tokens), expect $0.01-0.05 per run.