Agent skill · research science · davila7
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.
Why this skill is useful
Adds executable scripts for zero-shot image classification and cross-modal retrieval that extend the AI's capabilities in vision-language tasks.
What it needs
Requires pillow, torch, torchvision installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
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 Zero-shot classification Available models Model Parameters Speed Quality ------- ------------ ------- --------- RN50 102M Fast Good ViT-B/32 151M Medium Better ViT-L/14 428M Slow Best Image-text similarity Semantic image search Content moderation Batch processing Integration with vector databases Best practices 1. Use ViT-B/32 for most cases - Good balance 2. Normalize embeddings - Required for cosine similarity 3. Batch processing - More efficient 4. Cache embeddings - Expensive to recompute 5. Use descriptive labels - Better zero-shot performance 6. GPU recommended - 10-50× faster 7. Preprocess images - Use provided preprocess function Performance Operation CPU GPU (V100) ----------- ----- ------------ Image encoding ~200ms ~20ms Text encoding ~50ms ~5ms Similarity compute <1ms <1ms Limitations 1. Not for fine-grained tasks - Best for broad categories 2. Requires descriptive text - Vague labels perform poorly 3. Biased on web data - May have dataset biases 4. No bounding boxes - Whole image only 5. Limited spatial understanding - Position/counting weak Resources GitHub: https://github.com/openai/CLIP ⭐ 25,300+ Paper: https://arxiv.org/abs/2103.00020 Colab: https://colab.research.google.com/github/openai/clip/ License: MIT
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/multimodal-clip