Verified:GitHub

clip

Match images to natural-language descriptions for classification and retrieval.

Updated May 5, 2026

Search Tags:#semantic search #content moderation #clip #zero-shot classification #image-text similarity #vision-language embeddings

Ready to run

npx skills add https://github.com/Z43L/zeus-agent --skill clip-z43l

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/Z43L/zeus-agent/tree/main/optional-skills/mlops/clip
Command: npx skills add https://github.com/Z43L/zeus-agent --skill clip-z43l

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the need to interpret images using natural-language prompts without collecting labeled training data.

Core Features & Use Cases

Zero-shot image classification using text labels to categorize images instantly.
Image-text matching and semantic search by comparing image and text embeddings for retrieval.
Cross-modal use cases such as content moderation, deduplication, and lightweight visual question answering from whole-image signals.

Quick Start

Use the clip skill to classify the attached image into labels you provide as text (for example: a dog, a cat, a car).

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 labeled training data?▼

Zero-shot image classification matches images to natural-language descriptions using vision-language embeddings. You provide candidate text labels, and the model scores image-text similarity via cosine similarity to categorize images instantly without training data.

How does image-text similarity work for semantic image search?▼

Image-text similarity computes cross-modal embeddings from a CLIP model, comparing image preprocessing outputs against text tokenization results. Semantic image search ranks images by cosine-similarity scores, retrieving the closest visual matches to a given natural-language query.

Can I use natural-language prompts for content moderation workflows?▼

Content moderation workflows apply zero-shot image understanding by matching images against descriptive text labels for restricted content. This allows instant visual filtering and deduplication by scoring image-text similarity without collecting labeled training data.

What's the best way to store and query cross-modal embeddings for image retrieval?▼

Cross-modal embedding storage integrates image and text vectors into a vector database for retrieval. Querying the database with text tokenization outputs returns matching images ranked by cosine-similarity scoring, enabling efficient semantic image search.

Does batch processing support multiple images and candidate text labels?▼

Batch processing across many images and candidate text labels is supported. The system applies image preprocessing and text tokenization to compute vision-language embeddings, scoring image-text similarity for zero-shot classification across the entire batch.