| name | yt-deep-research |
| description | Search YouTube by keywords, transcribe the top 2 videos, and return a deep analysis of both. Triggers on: deep research, yt deep research, research and transcribe, search and transcribe youtube, deep dive youtube. |
Search YouTube, transcribe the top 2 results, and deliver a deep analysis.
This skill chains /yt-search and /transcribe together automatically.
Parsing Arguments
Parse $ARGUMENTS for:
- Keywords: One or more search terms. Required.
- --days: How many days back to include. Default:
30.
- --top: How many top results to show. Default:
15.
- --search-count: How many raw results to fetch from YouTube before filtering. Default:
50.
If the user just provides keywords with no flags, use the defaults.
Flow
Step 1: Run the YouTube Search
python3 ~/.claude/skills/yt-search/search_youtube.py <keywords> --days <days> --top <top> --search-count <search-count> --json
Wait for it to finish. Read the generated JSON from ~/content/research/.
Step 2: Present the Search Results
Show the user the top videos table (same as /yt-search output).
Step 3: Transcribe the Top 2 Videos
Take the top 2 videos by view count from the JSON results. Run transcription on both:
python3 ~/.claude/skills/transcribe/transcribe_video.py "<video_url_1>"
python3 ~/.claude/skills/transcribe/transcribe_video.py "<video_url_2>"
Run these sequentially (not in parallel) to avoid rate limits. Read both saved transcripts from ~/content/transcripts/.
Step 4: Deep Analysis
After reading both transcripts, provide a comprehensive analysis that covers:
Per-Video Breakdown (for each of the 2 transcribed videos)
- Hook (first 30 seconds): How do they open? What's the hook strategy?
- Structure: How is the video organized? (sections, transitions, story arc)
- Key talking points: The 3-5 main arguments or ideas
- Engagement tactics: Questions to the viewer, pattern interrupts, callbacks
- CTA placement: Where and how do they pitch? (mid-roll, end, soft vs hard)
- Quotable moments: 3-5 lines that would make great secondary text / clips
Cross-Video Comparison
- What do both videos have in common? (themes, structure, tone)
- Where do they differ?
- Which approach is more effective and why?
Content Opportunities
- What angles or subtopics are mentioned but not fully explored?
- What gaps exist that the user could fill with their own video?
- 3-5 specific video ideas based on what these creators covered (and didn't)
Applicable Patterns for the User
- Title patterns worth borrowing
- Hook strategies that would work for AI tools content
- Structural techniques to try
Step 5: Save the Analysis
Save the full analysis to:
~/content/research/<date>-<keywords>-deep-analysis.md
Include the video URLs, titles, and transcript file paths at the top for reference.
Step 6: Present to User
Show the user:
- The search results table
- Which 2 videos were transcribed
- The full deep analysis
- File paths for the report, transcripts, and analysis
Ask if they want to dig deeper into anything specific or use either transcript for /yt-package.
Rules
- All output goes to
~/content/research/
- Always pass
--json to the search script
- Only transcribe the top 2 by view count - don't transcribe more unless the user asks
- Don't hallucinate transcript content - only analyze what Whisper actually returned
- Keep analysis practical and actionable for content planning
- When suggesting ideas, remember the user makes AI tools content (Claude Code, etc.)
- If a transcription fails (private video, age-restricted, etc.), skip it and transcribe the next one in the list
