Verified:GitHub

clip

Embed images and text prompts into a shared space for zero-shot similarity matching.

4|Updated May 18, 2026

Search Tags:#semantic search #embeddings #content moderation #clip #zero-shot classification #cross-modal retrieval #image-text matching

Ready to run

npx skills add https://github.com/ZardLi1115/zedclaw --skill clip-zardli1115

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/mlops/clip
Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill clip-zardli1115

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, pillow, and includes references (resource) components.

What problem does it solve?

This Skill solves the problem of identifying, comparing, and searching images using natural-language labels without requiring task-specific training data.

Core Features & Use Cases

Zero-shot image understanding: Classify images into provided text categories by measuring image-text similarity.
Semantic image search: Retrieve the most relevant images for a text query by matching embeddings.
Cross-modal retrieval & moderation: Rank image-caption matches and support lightweight content categorization using similarity scores.

Quick Start

Use the clip skill to classify the image file "photo.jpg" against labels like a dog, a cat, a bird, and a car by running a zero-shot similarity comparison between image and tokenized text prompts.

Frequently Asked Questions about clip

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

FAQPage Schema

How do I classify images using natural language text labels without training data?▼

Zero-shot image classification matches images to natural language text labels by encoding both into a shared representation space and calculating cosine similarity scores to select top-matching categories.

Can I perform semantic image search by matching text queries to image embeddings?▼

Semantic image search retrieves relevant images for a text query by embedding both text prompts and images into a shared space, then comparing their similarity scores to rank results.

Does zero-shot image-text matching work with transformers and torch for cross-modal retrieval?▼

Zero-shot image-text matching uses transformers and torch to encode images and text prompts, producing cosine-like similarity scores between encoded features to rank image-caption matches for cross-modal retrieval.

What is the best way to do lightweight content moderation with natural language categories?▼

Lightweight content moderation categorizes images by measuring similarity against natural-language text labels, using zero-shot classification to rank image-text matches without requiring task-specific training data.

Do I need to install specific libraries to compute image-text similarity scores?▼

Computing image-text similarity scores requires installing CLIP-compatible libraries, primarily leveraging transformers, torch, and pillow to process image files and tokenize text prompts for encoding.

When should I not use zero-shot classification for image understanding tasks?▼

Zero-shot classification is not suitable when precise task-specific accuracy is required, as it relies on general image-text matching without dedicated training data to produce similarity-based predictions.