Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/curiositech/some_claude_skillsnpx agentmods add skills/curiositech/some_claude_skills/clip-aware-embeddingsWrote 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/clip-aware-embeddings)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/clip-aware-embeddings"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/clip-aware-embeddings/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/clip-aware-embeddings"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/clip-aware-embeddings.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 | $0.00082 | $0.02318 |
| Opus 5 | $0.00041 | $0.01159 |
| Sonnet 5 | $0.00016 | $0.00464 |
| Haiku 4.5 | $0.00008 | $0.00232 |
Grade A, and why
clip-aware-embeddings 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 12d 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLIP-Aware Image Embeddings
Smart image-text matching that knows when CLIP works and when to use alternatives.
MCP Integrations
| MCP | Purpose |
|---|---|
| Firecrawl | Research latest CLIP alternatives and benchmarks |
| Hugging Face (if configured) | Access model cards and documentation |
Quick Decision Tree
Your task:
├─ Semantic search ("find beach images") → CLIP ✓
├─ Zero-shot classification (broad categories) → CLIP ✓
├─ Counting objects → DETR, Faster R-CNN ✗
├─ Fine-grained ID (celebrities, car models) → Specialized model ✗
├─ Spatial relations ("cat left of dog") → GQA, SWIG ✗
└─ Compositional ("red car AND blue truck") → DCSMs, PC-CLIP ✗
When to Use This Skill
✅ Use for:
- Semantic image search
- Broad category classification
- Image similarity matching
- Zero-shot tasks on new categories
❌ Do NOT use for:
- Counting objects in images
- Fine-grained classification
- Spatial understanding
- Attribute binding
- Negation handling
Installation
pip install transformers pillow torch sentence-transformers --break-system-packages
Validation: Run python scripts/validate_setup.py
Basic Usage
Image Search
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
# Embed images
images = [Image.open(f"img{i}.jpg") for i in range(10)]
inputs = processor(images=images, return_tensors="pt")
image_features = model.get_image_features(**inputs)
# Search with text
text_inputs = processor(text=["a beach at sunset"], return_tensors="pt")
text_features = model.get_text_features(**text_inputs)
# Compute similarity
similarity = (image_features @ text_features.T).softmax(dim=0)
Common Anti-Patterns
Anti-Pattern 1: "CLIP for Everything"
❌ Wrong:
# Using CLIP to count cars in an image
prompt = "How many cars are in this image?"
# CLIP cannot count - it will give nonsense results
What ships with it
3 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.
- 12d ago First seen · 340 lines · 82 tokens per session scan A 4a3312af0776
clip-aware-embeddings is a skill published in the GitHub repository curiositech/some_claude_skills (221 stars, last pushed 6d ago), licensed MIT. It adds 82 tokens to every session and 2,318 once invoked, about $0.0004 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 2026-08-30.
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