Claude Code + NotebookLM + Obsidian = GOD MODE

Summary of Video Subtitles (Tech Concepts / Workflow)

The video demonstrates a practical “research workflow” that combines Claude Code, NotebookLM, Obsidian, and Claude Code’s Skill Creator to automate end-to-end research and content deliverables.

The core idea is to turn multiple steps (source collection → analysis → deliverables → documentation) into one reusable “super skill”, then store results in an Obsidian vault so Claude Code can improve over time.

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What the combined workflow does (“research on steroids”)

1. Collect sources in Claude Code
   • Example source: YouTube videos.
2. Use a specific Claude Code skill to fetch/structure video data
   • Example approach: use yt-dlp for YouTube search.
3. Send that data to NotebookLM
   • NotebookLM is called from Claude Code through skills.
4. NotebookLM performs analysis and can produce deliverables, such as:
   • podcast / video / infographic / slide deck (and others explicitly listed)
   • mind map, flash cards, audio review, infographic, etc.
5. Return results to Claude Code, then store them as markdown/text files in an Obsidian vault.

The creator emphasizes flexibility: the “source” and even the NotebookLM part can be swapped for other inputs/flows as long as the template stays the same.

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How the tooling is connected

1) Claude Code + Skill Creator

• You create sub-skills using commands like /skill creator.
• Example: build a YouTube search skill:
  • searches videos by query using yt-dlp
  • returns structured results
• Then build a second layer: combine sub-skills into a single pipeline/super-skill that:
  • calls the YouTube search skill
  • sends results to NotebookLM
  • returns the requested deliverable (e.g., infographic)

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2) NotebookLM integration (via GitHub repo / CLI)

• NotebookLM has no public-facing API, so integration uses a GitHub tool (“notebook LM-py”).
• Steps described:
  • install the repo via terminal commands
  • authenticate via a “space login” flow (browser opens for NotebookLM login)
  • use/install a NotebookLM skill (via a NotebookLM skill install), or instruct Skill Creator to create the NotebookLM skill from the repo

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3) Obsidian as the persistent “memory” layer

• Claude Code outputs analysis results into an Obsidian vault as markdown files.
• Benefits highlighted:
  • humans can browse and connect notes (links/backlinks, graphs)
  • Claude Code can “see” the structure via transparent markdown files
  • enables a long-term improvement loop

The video describes a symbiotic/self-improving loop:

• Run the workflow repeatedly
• Store new analysis + deliverables in the vault
• Over time, Claude Code learns/refines how it communicates and thinks
• The claude.md file acts like a “brain within a brain,” capturing conventions and preferences
  • It can be updated based on new conversations to maintain output style over time.

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Setup + Execution steps (tutorial-style)

• Install Skill Creator in Claude Code:
  • use /plugin
  • search/install Skill Creator
  • restart Claude Code afterward
• Create skills:
  • use /skill creator
  • describe desired behavior (example: a YouTube search skill that returns structured results)
  • optionally skip or run tests/evals
• Create NotebookLM skill via GitHub integration tool:
  • install “notebook LM-py” with terminal commands
  • authenticate with NotebookLM
  • either install a NotebookLM skill directly or instruct Skill Creator to create it from the repo/commands
• Combine into a pipeline super-skill:
  • use Skill Creator again to stitch YouTube → NotebookLM → deliverable into one skill
• Run the pipeline:
  • example prompt: search YouTube for content about “MCP servers / Claude Code / MCP”
  • request:
    • top five MCP servers
    • analysis of what drives views, outliers, gaps, opportunities
  • request: generate an infographic

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Performance / output timing

• NotebookLM analysis for text: fairly quick (example: ~6 minutes)
• Deliverables like slide decks: can take longer (example: up to ~15 minutes)
• Infographics: take “a handful of minutes”

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Example output described

• The produced infographic (for “MCP”) includes items/companies such as:
  • Supabase
  • Context 7
  • Playwright
  • plus other named tooling mentioned in results (e.g., Figma, Sentry, PostHog)
• The research also returns a full markdown file containing:
  • key takeaways
  • server entries
  • double-bracket links compatible with Obsidian navigation
  • backreferences and graph connectivity inside Obsidian

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Main speakers/sources

• Speaker/creator: The video narrator (speaker uses “I” throughout; no name provided in subtitles)
• Key external sources/tools mentioned:
  • NotebookLM (integrated via GitHub repo “notebook LM-py”)
  • yt-dlp (YouTube searching)
  • Obsidian (vault/markdown storage)
  • Claude Code’s Skill Creator
• Sponsor mentioned: “Yours truly” / promotion for Chase AI Plus and the Chase AI community (no separate human sponsor name given in subtitles)