photo-content-recognition-curation-expert

Q: How do I install photo-content-recognition-curation-expert?

Run npx skills add https://github.com/curiositech/some_claude_skills --skill photo-content-recognition-curation-expert in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/photo-content-recognition-curation-expert/ (personal) or .claude/skills/photo-content-recognition-curation-expert/ (project).

Q: Which AI agents can use photo-content-recognition-curation-expert?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Q: Is photo-content-recognition-curation-expert safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Q: Who maintains photo-content-recognition-curation-expert?

It is published by curiositech in curiositech/some_claude_skills. It has 332 recorded installs.

by curiositechMedia & Creative

Expert in photo content recognition, intelligent curation, and quality filtering. Specializes in face/animal/place recognition, perceptual hashing for de-duplication, screenshot/meme detection, burst photo selection, and quick indexing strategies. Activate on 'face recognition', 'face clustering', 'perceptual hash', 'near-duplicate', 'burst photo', 'screenshot detection', 'photo curation', 'photo indexing', 'NSFW detection', 'pet recognition', 'DINOHash', 'HDBSCAN faces'. NOT for GPS-based location clustering (use event-detection-temporal-intelligence-expert), color palette extraction (use color-theory-palette-harmony-expert), semantic image-text matching (use clip-aware-embeddings), or video analysis/frame extraction.

Installs: 332
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/curiositech/some_claude_skills --skill photo-content-recognition-curation-expert

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads photo-content-recognition-curation-expert automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

What do you need to recognize/filter?
│
├─ Duplicate photos? ─────────────────────────────── Perceptual Hashing
│   ├─ Exact duplicates? ──────────────────────────── dHash (fastest)
│   ├─ Brightness/contrast changes? ───────────────── pHash (DCT-based)
│   ├─ Heavy crops/compression? ───────────────────── DINOHash (2025 SOTA)
│   └─ Production system? ─────────────────────────── Hybrid (pHash → DINOHash)
│
├─ People in photos? ─────────────────────────────── Face Clustering
│   ├─ Known thresholds? ──────────────────────────── Apple-style Agglomerative
│   └─ Unknown data distribution? ─────────────────── HDBSCAN
│
├─ Pets/Animals? ─────────────────────────────────── Pet Recognition
│   ├─ Detection? ─────────────────────────────────── YOLOv8
│   └─ Individual clustering? ─────────────────────── CLIP + HDBSCAN
│
├─ Best from burst? ──────────────────────────────── Burst Selection
│   └─ Score: sharpness + face quality + aesthetics
│
└─ Filter junk? ──────────────────────────────────── Content Detection
    ├─ Screenshots? ───────────────────────────────── Multi-signal classifier
    └─ NSFW? ──────────────────────────────────────── Safety classifier

Core Concepts

1. Perceptual Hashing for Near-Duplicate Detection

Problem: Camera bursts, re-saved images, and minor edits create near-duplicates.

Solution: Perceptual hashes generate similar values for visually similar images.

Method Comparison:

Method	Speed	Robustness	Best For
dHash	Fastest	Low	Exact duplicates
pHash	Fast	Medium	Brightness/contrast changes
DINOHash	Slower	High	Heavy crops, compression
Hybrid	Medium	Very High	Production systems

Hybrid Pipeline (2025 Best Practice):

Stage 1: Fast pHash filtering (eliminates obvious non-duplicates)
Stage 2: DINOHash refinement (accurate detection)
Stage 3: Optional Siamese ViT verification

Hamming Distance Thresholds:

Conservative: ≤5 bits different = duplicates
Aggressive: ≤10 bits different = duplicates

→ Deep dive: references/perceptual-hashing.md

2. Face Recognition & Clustering

Goal: Group photos by person without user labeling.

Apple Photos Strategy (2021-2025):

Extract face + upper body embeddings (FaceNet, 512-dim)
Two-pass agglomerative clustering
Conservative first pass (threshold=0.4, high precision)
HAC second pass (threshold=0.6, increase recall)
Incremental updates for new photos

HDBSCAN Alternative:

No threshold tuning required
Robust to noise
Better for unknown data distributions

Parameters:

Setting	Agglomerative	HDBSCAN
Pass 1 threshold	0.4 (cosine)	-
Pass 2 threshold	0.6 (cosine)	-
Min cluster size	-	3 photos
Metric	cosine	cosine

→ Deep dive: references/face-clustering.md

3. Burst Photo Selection

Problem: Burst mode creates 10-50 nearly identical photos.

Multi-Criteria Scoring:

Criterion	Weight	Measurement
Sharpness	30%	Laplacian variance
Face Quality	35%	Eyes open, smiling, face sharpness
Aesthetics	20%	NIMA score
Position	10%	Middle frames bonus
Exposure	5%	Histogram clipping check

Burst Detection: Photos within 0.5 seconds of each other.

→ Deep dive: references/content-detection.md

4. Screenshot Detection

Multi-Signal Approach:

Signal	Confidence	Description
UI elements	0.85	Status bars, buttons detected
Perfect rectangles	0.75	>5 UI buttons (90° angles)
High text	0.70	>25% text coverage (OCR)
No camera EXIF	0.60	Missing Make/Model/Lens
Device aspect	0.60	Exact phone screen ratio
Perfect sharpness	0.50	>2000 Laplacian variance

Decision: Confidence >0.6 = screenshot

→ Deep dive: references/content-detection.md

5. Quick Indexing Pipeline

Goal: Index 10K+ photos efficiently with caching.

Features Extracted:

Perceptual hashes (de-duplication)
Face embeddings (people clustering)
CLIP embeddings (semantic search)
Color palettes
Aesthetic scores

Performance (10K photos, M1 MacBook Pro):

Operation	Time
Perceptual hashing	2 min
CLIP embeddings	3 min (GPU)
Face detection	4 min
Color palettes	1 min
Aesthetic scoring	2 min (GPU)
Clustering + dedup	1 min
Total (first run)	~13 min
Incremental	<1 min

→ Deep dive: references/photo-indexing.md

Common Anti-Patterns

Anti-Pattern: Euclidean Distance for Face Embeddings

What it looks like:

distance = np.linalg.norm(embedding1 - embedding2)  # WRONG

Why it's wrong: Face embeddings are normalized; cosine similarity is the correct metric.

What to do instead:

from scipy.spatial.distance import cosine
distance = cosine(embedding1, embedding2)  # Correct

Anti-Pattern: Fixed Clustering Thresholds

What it looks like: Using same distance threshold for all face clusters.

Why it's wrong: Different people have varying intra-class variance (twins vs. diverse ages).

What to do instead: Use HDBSCAN for automatic threshold discovery, or two-pass clustering with conservative + relaxed passes.

Anti-Pattern: Raw Pixel Comparison for Duplicates

What it looks like:

is_duplicate = np.allclose(img1, img2)  # WRONG

Why it's wrong: Re-saved JPEGs, crops, brightness changes create pixel differences.

What to do instead: Perceptual hashing (pHash or DINOHash) with Hamming distance.

Anti-Pattern: Sequential Face Detection

What it looks like: Processing faces one photo at a time without batching.

Why it's wrong: GPU underutilization, 10x slower than batched.

What to do instead: Batch process images (batch_size=32) with GPU acceleration.

Anti-Pattern: No Confidence Filtering

What it looks like:

for face in all_detected_faces:
    cluster(face)  # No filtering

Why it's wrong: Low-confidence detections create noise clusters (hands, objects).

What to do instead: Filter by confidence (threshold 0.9 for faces).

Anti-Pattern: Forcing Every Photo into Clusters

What it looks like: Assigning noise points to nearest cluster.

Why it's wrong: Solo appearances shouldn't pollute person clusters.

What to do instead: HDBSCAN/DBSCAN naturally identifies noise (label=-1). Keep noise separate.

Quick Start

from photo_curation import PhotoCurationPipeline

pipeline = PhotoCurationPipeline()

# Index photo library
index = pipeline.index_library('/path/to/photos')

# De-duplicate
duplicates = index.find_duplicates()
print(f"Found {len(duplicates)} duplicate groups")

# Cluster faces
face_clusters = index.cluster_faces()
print(f"Found {len(face_clusters)} people")

# Select best from bursts
best_photos = pipeline.select_best_from_bursts(index)

# Filter screenshots
real_photos = pipeline.filter_screenshots(index)

# Curate for collage
collage_photos = pipeline.curate_for_collage(index, target_count=100)

Python Dependencies

torch transformers facenet-pytorch ultralytics hdbscan opencv-python scipy numpy scikit-learn pillow pytesseract

Integration Points

event-detection-temporal-intelligence-expert: Provides temporal event clustering for event-aware curation
color-theory-palette-harmony-expert: Extracts color palettes for visual diversity
collage-layout-expert: Receives curated photos for assembly
clip-aware-embeddings: Provides CLIP embeddings for semantic search and DeepDBSCAN

References

DINOHash (2025): "Adversarially Fine-Tuned DINOv2 Features for Perceptual Hashing"
Apple Photos (2021): "Recognizing People in Photos Through Private On-Device ML"
HDBSCAN: "Hierarchical Density-Based Spatial Clustering" (2013-2025)
Perceptual Hashing: dHash (Neal Krawetz), DCT-based pHash

Version: 2.0.0 Last Updated: November 2025

FAQ

What does the photo-content-recognition-curation-expert skill do?

How do I install photo-content-recognition-curation-expert?

Run npx skills add https://github.com/curiositech/some_claude_skills --skill photo-content-recognition-curation-expert in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/photo-content-recognition-curation-expert/ (personal) or .claude/skills/photo-content-recognition-curation-expert/ (project).

Which AI agents can use photo-content-recognition-curation-expert?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is photo-content-recognition-curation-expert safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains photo-content-recognition-curation-expert?

It is published by curiositech in curiositech/some_claude_skills. It has 332 recorded installs.