Notebooklm

--- description: Vault-first, source-grounded research via Gemini File Search. One command, no browser. The grounded counterpart to /research-deep (which uses the 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. Determine the topic based on the user’s argument. If no topic is provided, ask: “What topic for source-grounded research?” 2. Run the Python command from the repo root (~/Projects/personal/obsidian-second-brain/): ``bash uv run -m scripts.research.notebooklm --topic "<topic>" 3. 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 <<<NOTEBOOKLMPROPAGATIONPAYLOAD>>> JSON block. 4. **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. 5. Report back to the user: "Saved [[YYYY-MM-DD - <slug>]] to Research/NotebookLM/. Linked from today's daily note. Updated [[X]], created [[Y]]." 6. Plain English triggers: "notebooklm this", "ground research on X using my vault", "source-grounded research on X", "ask my own notes about X". 7. 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. 8. Configuration: requires GEMINIAPIKEY 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) provides the same architectural framework: source-grounded retrieval, multi-document context, citation-style synthesis. One HTTP call, no manual paste step. Cost: $0.15 per million tokens indexed, free storage, generation at standard Gemini token rates. For a 12-note vault bundle (~30K tokens), expect $0.01:0.05 per run.