photo-content-recognition-curation-expert

What problem does it solve?

This Skill provides expert capabilities for analyzing photo content and automating curation. It combines traditional computer vision with modern deep learning to recognize faces, animals, and scenes, cluster people, detect duplicates, select the best burst frames, and index large photo libraries efficiently. It reduces manual cataloging time and helps build high-quality organized collections.

Core Features & Use Cases

• Face recognition & clustering (Apple Photos-style approaches)
• Pet/animal recognition and clustering
• Near-duplicate detection using perceptual hashing (DINOHash, pHash, dHash)
• Burst photo selection with multi-criteria scoring (sharpness, faces, aesthetics)
• Content detection (screenshots, memes, NSFW filtering)
• Quick indexing for large libraries (10K+ photos) with batch processing
• Aesthetic quality scoring (e.g., NIMA)
• Fast indexing pipelines and scalable storage of embeddings and metadata

Quick Start

• Use for typical photo libraries to identify people, duplicates, or best shots.
• Example prompts: "Run face recognition on the album and create person clusters." OR "Index 10K photos and detect duplicates."