clip

Classify images and score image-text similarity using CLIP embeddings.

Updated Feb 15, 2026

Search Tags:#semantic search #zero-shot #clip #vision-language #image classification #cross-modal retrieval

Ready to run

npx skills add https://github.com/ricable/mcai --skill clip-ricable

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/ricable/mcai/tree/main/.agents/skills/clip
Command: npx skills add https://github.com/ricable/mcai --skill clip-ricable

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

CLIP connects images and text so you can identify, compare, and retrieve visual content without training a custom classifier.

Core Features & Use Cases

Zero-shot image classification for fast label assignment with no task-specific training data.
Image-text matching and semantic search for finding the most relevant photo, screenshot, or product image from a catalog.
Content moderation and visual QA workflows where you need broad, prompt-driven judgments over large image sets.

Quick Start

Load CLIP with a vision transformer, pass in an image and a short list of labels, and read the similarity scores to see which label best matches the image.

Frequently Asked Questions about clip

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform zero-shot image classification without training data?▼

Zero-shot image classification assigns labels to images without task-specific training by computing normalized cosine similarity between CLIP embeddings of the image and your descriptive text prompts.

Can I use CLIP for image-text similarity scoring in semantic search?▼

Yes, CLIP performs image-text similarity scoring and cross-modal retrieval for semantic search by generating normalized embeddings for both text queries and image collections to find relevant visual content.

How does cross-modal retrieval work for visual search workflows?▼

Cross-modal retrieval processes text queries and image collections through CLIP to generate comparable embeddings, using normalized cosine similarity to match and retrieve the most relevant images from your catalog.

What is the best way to moderate visual content across large image sets?▼

Content moderation and visual QA workflows apply prompt-driven judgments over large image sets by passing descriptive moderation labels and images through CLIP to evaluate broad similarity scores.

Does zero-shot image classification require PyTorch and torchvision?▼

Yes, zero-shot image classification using CLIP requires PyTorch, torchvision, Pillow, and transformers to process image inputs, generate CLIP embeddings, and calculate normalized cosine similarity against text prompts.

When should I avoid using CLIP for image classification?▼

You should avoid using CLIP for image classification when your task requires detecting highly specialized, fine-grained categories not covered by descriptive text prompts, as zero-shot matching depends on broad semantic similarity.