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Photo Content Recognition Curation Expert

This handles the computer vision pipeline for photo libraries: face clustering without manual tagging, perceptual hashing to catch near-duplicates (it uses DINOHash for heavy crops, pHash for speed), and burst photo selection that scores sharpness plus face quality. The hybrid deduplication approach is smart, running fast pHash first then DINOHash refinement. It'll also filter screenshots and detect pets. What I like is the two-pass face clustering strategy borrowed from Apple Photos, conservative first at 0.4 cosine distance, then relaxed at 0.6 to catch more. They claim 13 minutes to index 10K photos on an M1, under a minute for incremental updates. Good for building a photo organizer or cleaning up messy camera rolls.

skills.sh installs
227
GitHub stars
113
last updated
Jun 3, 2026

Install

$ npx -y skills add erichowens/some_claude_skills --skill photo-content-recognition-curation-expert --agent claude-code

Installs into .claude/skills of the current project.

Files

Photo Content Recognition & Curation Expert

Expert in photo content analysis and intelligent curation. Combines classical computer vision with modern deep learning for comprehensive photo analysis.

When to Use This Skill

✅ Use for:

  • Face recognition and clustering (identifying important people)
  • Animal/pet detection and clustering
  • Near-duplicate detection using perceptual hashing (DINOHash, pHash, dHash)
  • Burst photo selection (finding best frame from 10-50 shots)
  • Screenshot vs photo classification
  • Meme/download filtering
  • NSFW content detection
  • Quick indexing for large photo libraries (10K+)
  • Aesthetic quality scoring (NIMA)

❌ NOT for:

  • GPS-based location clustering → event-detection-temporal-intelligence-expert
  • Color palette extraction → color-theory-palette-harmony-expert
  • Semantic image-text matching → clip-aware-embeddings
  • Video analysis or frame extraction

Quick Decision Tree

Sponsors

Details

Repository
github.com/erichowens/some_claude_skills
First seen
Jun 3, 2026

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