clip
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CLIP - Contrastive Language-Image Pre-Training OpenAI's model that understands images from natural language. ## When to use CLIP **Use when:** - Zero-shot image classification (no training data needed) - Image-text similarity/matching - Semantic image search - Content moderation (detect NSFW, violence) - Visual question answering - Cross-modal retrieval (image→text, text→image) **Metrics**: - **25,300+ GitHub stars** - Trained on 400M image-text pairs - Matches ResNet-50 on ImageNet (zero-shot) - MIT License **Use alternatives instead**: - **BLIP-2**: Better captioning - **LLaVA**: Vision-language chat - **Segment Anything**: Image segmentation ## Quick start ### Installation ```bash pip install git+https://github.com/openai/CLIP.git pip install torch torchvision ftfy regex tqdm ``` ### Zero-shot classification ```python import torch import clip from PIL import Image # Load model device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load("ViT-B/32", device=device) # Load image image = preprocess(Image.open("photo.jpg")).unsqueeze(0).to(device) # Define possible labels text = clip.tokenize(["a dog", "a cat", "a bird", "a car"]).to(device) # Compute simil
- When to use CLIP
- Quick start
- Installation
- Zero-shot classification
- Available models
- Image-text similarity
- Semantic image search
- Content moderation
- Batch processing
- Integration with vector databases
- Best practices
- Performance
- Limitations
- Resources
pip install git+https://github.com/openai/CLIP.git pip install torch torchvision ftfy regex tqdm
What does the clip skill do?
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.