clip

Classify images and retrieve matching visual content using pretrained CLIP models.

Updated Apr 10, 2026

Search Tags:#semantic search #embeddings #zero-shot #clip #vision-language #image classification

Ready to run

npx skills add https://github.com/VYRE-Studios/Windows-Agentic-Framework --skill clip-vyre-studios

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/VYRE-Studios/Windows-Agentic-Framework/tree/main/skills/mlops/models/clip
Command: npx skills add https://github.com/VYRE-Studios/Windows-Agentic-Framework --skill clip-vyre-studios

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The CLIP skill provides zero-shot visual understanding, letting users classify images, match image‑text pairs, and search across visual data without any model fine‑tuning.

Core Features & Use Cases

Zero‑shot classification: Identify image categories instantly with only textual labels.
Image‑text similarity: Compute embeddings to retrieve matching images for a textual query.
Semantic search & moderation: Perform content moderation and similarity search across large image collections.

Quick Start

Use the CLIP skill to retrieve the top three images matching the description "sunset over the ocean" from the directory "images/".

Frequently Asked Questions about clip

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

FAQPage Schema

How do I classify images without providing training data?▼

Zero-shot image classification uses pretrained CLIP models to identify image categories instantly with only textual labels, eliminating the need for custom training data or model fine-tuning.

Can I perform semantic search across my image collection using text queries?▼

Semantic search across image collections is achieved by computing normalized embeddings to retrieve matching images for a textual query, enabling efficient cosine similarity computation on GPU or CPU.

What is zero-shot image-text similarity matching?▼

Zero-shot image-text similarity matching computes embeddings to evaluate how well a textual description aligns with an image, allowing you to retrieve matching visual content without any fine-tuning.

Does zero-shot image classification require a GPU to run?▼

Zero-shot image classification with pretrained CLIP models supports efficient cosine similarity computation on both GPU and CPU, so a dedicated GPU is not strictly required.

Can I use this for content moderation across large image directories?▼

Content moderation and similarity search can be performed across large image collections by applying zero-shot classification to filter and retrieve inappropriate visual data using textual labels.

What is the best way to retrieve images matching a specific description from a directory?▼

The best way to retrieve matching images is applying semantic search with CLIP to compute embeddings for a textual query and returning the top matching visual content from the specified image directory.