Research Pipeline

Research Pipeline Skill

A super-skill that combines YouTube search + NotebookLM analysis into a single research workflow. Searches YouTube for videos, sends them to NotebookLM for deep analysis, optionally generates a deliverable (infographic, podcast, slide deck, etc.), and saves everything to the Obsidian vault.

Token Budget

This pipeline is designed to use zero Claude tokens for analysis. NotebookLM processes the corpus server-side (Google's infrastructure). Claude's only job is to orchestrate the pipeline, capture notebooklm ask stdout, and write the result to Obsidian. Never re-read large scraped documents in Claude — pass them directly to NotebookLM and let it do the analysis.

Trigger

Use this skill when the user wants to:

• Research a topic using YouTube as the source
• Analyze trends, gaps, and insights from YouTube videos on a subject
• Generate research deliverables (infographic, podcast, summary) from YouTube content
• Do content research (what's working, view patterns, gaps to exploit)

Explicit triggers:

• /research-pipeline
• "research [topic] on YouTube"
• "analyze YouTube videos about [topic]"
• "YouTube pipeline for [topic]"
• "find and analyze top videos about [topic]"

Full Pipeline

Step 1 — YouTube Search (youtube-search skill)

Search YouTube for the query and collect video URLs + metadata.

yt-dlp "ytsearch{N}:{QUERY}" --dump-json --no-download --flat-playlist --no-warnings 2>/dev/null

Parse results → extract video IDs → build URLs.

Default: 10 videos. User can specify (e.g., "top 5", "top 20").

Step 2 — Create NotebookLM Notebook

Create a dedicated notebook for this research session:

notebooklm create "{QUERY} Research - {DATE}"

Save the notebook ID from output.

Step 3 — Add YouTube Sources

Add each video URL as a source to the notebook (up to 50):

notebooklm source add "https://www.youtube.com/watch?v={ID}"

Add all videos in sequence. NotebookLM will process the transcripts automatically.

Step 4 — Wait for Processing

Sources take 30-60 seconds to process. Check status before querying:

notebooklm source list | grep -E "pending|processing"
# If all rows show "ready", proceed. Otherwise wait 30s and recheck.

Step 5 — Generate Analysis via NotebookLM (zero Claude tokens)

IMPORTANT: Capture the output — do NOT read it into Claude context. Pipe stdout directly to a temp file, then write it into the Obsidian note.

# Run analysis and capture to temp file (NotebookLM processes server-side)
notebooklm ask "Analyze these YouTube videos and provide:
1. KEY THEMES: What are the main topics covered across all videos?
2. TOP INSIGHTS: What are the most important insights or takeaways?
3. CREATOR PERSPECTIVES: How do different creators approach the same topics?
4. GAPS & OPPORTUNITIES: What topics are NOT covered well? What's missing?
5. OUTLIERS: Any surprising or counterintuitive findings?
6. ACTIONABLE TAKEAWAYS: What can someone do with this information?" > /tmp/notebooklm_analysis.txt 2>&1

echo "Analysis captured: $(wc -c < /tmp/notebooklm_analysis.txt) chars"

Step 6 — Generate Deliverable (if requested)

If user requested a specific deliverable, run in parallel with or after Step 5:

Download to Research folder:

RESEARCH_DIR="04_Resources/Research"
notebooklm download infographic "${RESEARCH_DIR}/{QUERY}-{DATE}-infographic.png"
notebooklm download audio "${RESEARCH_DIR}/{QUERY}-{DATE}-podcast.mp3"

Step 7 — Save to Obsidian Vault

Write the final note by reading from the temp file — never re-read the full corpus in Claude.

Save to: 04_Resources/Research/{QUERY}-{DATE}.md

RESEARCH_DIR="04_Resources/Research"
OUTPUT="${RESEARCH_DIR}/{QUERY}-{DATE}.md"
ANALYSIS=$(cat /tmp/notebooklm_analysis.txt)

cat > "${OUTPUT}" << EOF
---
title: Research — {QUERY}
date: {DATE}
tags: [research, youtube, {topic-tags}]
source: youtube
videos_analyzed: {N}
notebooklm_notebook: {NOTEBOOK_ID}
---

# Research: {QUERY}

## Videos Analyzed

| # | Title | Channel | URL |
|---|-------|---------|-----|
{TABLE}

## Analysis

${ANALYSIS}

## Deliverables

{DELIVERABLE_LINKS_IF_ANY}
EOF

echo "Saved: ${OUTPUT}"
rm /tmp/notebooklm_analysis.txt

Parameters

• query — Research topic (required)
• limit — Number of videos to analyze (default: 10)
• deliverable — Optional: infographic, podcast, slides, mindmap, flashcards
• save_to — Output path in vault (default: 04_Resources/Research/)

Example Prompts

/research-pipeline Claude Code MCP servers

Research the top 10 YouTube videos about AI agents and give me an infographic

YouTube pipeline: find 5 videos about Obsidian workflows, analyze gaps

Notes

• notebooklm CLI must be authenticated: run notebooklm login in terminal first
• Deliverables can take 5-15 minutes (NotebookLM processes async)
• Use --wait flag to block until deliverable is ready
• Save deliverable files to 04_Resources/Meetings/Research/ alongside the markdown note
• The NotebookLM analysis is done server-side by Google — no Claude tokens used for that step
• yt-dlp installed at: /opt/homebrew/bin/yt-dlp

Adapting to Other Sources

This pipeline isn't just for YouTube. Swap Step 1 for any source:

• PDFs: notebooklm source add "./file.pdf"
• Web articles: notebooklm source add "https://article-url.com"
• Text: notebooklm source add --text "paste content here"
• Drive files: notebooklm source add "https://drive.google.com/..."