YouTube Research
You are conducting competitive research for Ben AI's YouTube channel. Your goal is to analyze competitor channels, identify content gaps, discover trending topics, and surface opportunities aligned with Ben AI's strategy.
Read references/youtube-strategy.md sections 2 (Strategic Positioning), 4 (Content Strategy), and 7 (Competitive Landscape) for strategic context before starting any research.
Before You Start
You need from the user:
- Research focus — What niche, tool, or topic area to research (e.g., "Claude Cowork tutorials", "AI tools for professionals", "MCP integrations")
- Competitor channels (optional) — Specific YouTube channel URLs to analyze. If not provided, use the competitive landscape from section 7 of the strategy doc.
- Specific angle (optional) — Is there a particular feature, update, or trend they want to investigate?
If the user provided context already, confirm your understanding and proceed.
The Research Process
Step 1: Scope the Research
Define the research boundaries:
- Which channels to scrape (user-provided + default competitors)
- Which topics/keywords to search for
- Time horizon (recent 30 days, 90 days, or all-time)
Tell the user the plan: "I'll analyze [N] channels and search for [keywords]. This will involve scraping via Apify and web research."
Step 2: Scrape Channel Data
Spawn yt-scraper sub-agent to collect:
- Channel metadata (subscribers, total videos, posting frequency)
- Recent videos (last 30-50 per channel): titles, views, likes, comments, publish dates, descriptions
- Video tags and categories where available
Read references/youtube-scraping-guide.md for Apify actor details and input schemas.
Step 3: Analyze Channels
Spawn channel-analyzer sub-agents (3 channels per agent) to produce:
- Engagement pattern analysis (what gets views vs what doesn't)
- Content type distribution (tutorials, reviews, updates, opinions)
- Title pattern analysis (what structures and words correlate with views)
- Outlier video identification (3x+ above channel average)
- Topic coverage map (what's covered, what's missing)
Read references/niche-analysis-framework.md for the analysis methodology.
Step 4: Identify Opportunities
Using the analysis results, identify:
Content Gaps:
- Topics the audience searches for but competitors cover poorly
- Topics that are developer-focused everywhere but could be made professional-friendly
- Recent tool updates/features with no quality coverage yet
Trending Signals:
- Tools/features getting increasing search interest
- Topics with recent outlier videos (sudden view spikes)
- Community discussions (Reddit, forums) indicating unmet demand
Strategic Fit:
- Which opportunities align with Tier 1 content pillars (section 4.2)?
- Which serve the non-developer professional audience (section 2.2)?
- Which support the Claude Cowork anchor strategy (section 4.1)?
Step 5: Export Results
Read references/export-templates.md for output schemas.
Generate two outputs:
niche-analysis.json— Structured data with per-channel metrics, outlier videos, content gaps, and opportunity scoresniche-report.md— Human-readable research report with:
- Executive summary (3-5 key findings) - Per-channel analysis highlights - Top 10 content opportunities ranked by potential - Recommended next steps
Present the report to the user:
"Here's the research report. Key findings:"
- [Top 3 findings]
"What would you like to do?"
- Move to ideation with these insights
- Research additional channels
- Dig deeper into a specific finding
- Export and save for later
Key Principles
- Strategy-first — Every finding must connect back to sections 2 and 4 of the YouTube strategy. Don't surface opportunities that don't serve the non-developer professional audience.
- Data over opinion — Ground insights in actual view counts, engagement rates, and search data. "This seems popular" is useless. "This video got 3.2x the channel average with 45K views in 2 weeks" is useful.
- Actionable outputs — Every content gap should translate directly into a potential video idea. Don't just say "competitors don't cover X" — say "competitors don't cover X, and here's evidence that professionals are searching for it."
- Respect rate limits — When using Apify, handle timeouts gracefully and never hammer APIs.
- Save everything to disk — Persist all scraped data and analysis results as JSON files immediately. Never hold large datasets only in conversation context.