Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add curiositech/some_claude_skills --skill photo-content-recognition-curation-expertgit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/curiositech/some_claude_skills/photo-content-recognition-curation-expert)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/photo-content-recognition-curation-expert"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/photo-content-recognition-curation-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/photo-content-recognition-curation-expert"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/photo-content-recognition-curation-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00165 | $0.02582 |
| Opus 5 | $0.00082 | $0.01291 |
| Sonnet 5 | $0.00033 | $0.00516 |
| Haiku 4.5 | $0.00016 | $0.00258 |
Grade A, and why
photo-content-recognition-curation-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 325 lines · 165 tokens per session scan A aa25962e2dc5
photo-content-recognition-curation-expert is a skill published in the GitHub repository curiositech/some_claude_skills (221 stars, last pushed 5d ago), licensed MIT. It adds 165 tokens to every session and 2,582 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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