🧠 ThumbScout

ThumbScout is an AI-powered research agent (in development) designed to analyze top-performing YouTube thumbnails for visual trends, emotional hooks, and formatting strategies. The system aims to help creators make smarter packaging decisions based on real-world viewer behavior.

While this repository is still in early planning stages, it serves as the future home for a complete, end-to-end thumbnail research and analysis tool.

🔧 Note: The prototype MVP for title/hook analysis has been moved to a standalone project: TrendScout. This served as a lightweight POC for text-only trend analysis and now lives as Vibe Agent 01: TrendScout.

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🔭 Future Scope (ThumbScout v1+)

🔍 Inputs

• YouTube video topic or niche
• Optional: competitor channels or specific video URLs

🧠 Agent Capabilities

• Scrape YouTube/Google search results via SerpAPI
• Analyze thumbnails using GPT-4 Vision
• Detect visual styles, emotional cues, text placement, color schemes
• Identify patterns (e.g. faces, objects, emojis, font types)
• Score each image on clickability or trend alignment

📊 Output Formats

• Markdown trend summary (copy/paste or export)
• Image grid of top thumbnails
• CSV or Notion export with visual descriptors
• Optional: A/B test results overlay for feedback loop

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🛠️ Tech Stack

• LLMs: GPT-4 (Vision) or Claude 3 Opus
• Scraping: SerpAPI
• Orchestration: n8n (Cloud or Self-Hosted)
• Optional Storage: Notion DB, Google Drive, Firebase

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🚧 Status

ThumbScout is currently in design and prototyping. You can preview early trend analysis logic inside TrendScout, which handles video title analysis and GPT-generated trend summaries. The visual analysis layer is planned for v1.1+.

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💬 Questions? Ideas? DM @RosTalbot or fork the repo to contribute.