Verified:GitHub

clip

Classifies images and finds matches using CLIP's multimodal embeddings.

1|Updated May 16, 2026

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

Ready to run

npx skills add https://github.com/devMoez/titan --skill clip-devmoez

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you understand images using natural-language text, so you can classify, search, and match visuals without collecting labeled training data.

Core Features & Use Cases

Zero-shot image classification: Assign labels to images using text prompts alone.
Image-text matching & retrieval: Find the best image for a given query (and vice versa).
Multimodal content moderation: Estimate categories such as safe/unsafe or violent/graphic using prompt-based labels.
Use case: You have a folder of product photos and want to retrieve the most “red sneakers” images using only text queries.

Quick Start

Use the clip skill to run zero-shot classification by comparing your image against a list of label 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 text prompts without collecting labeled training data?▼

Zero-shot image classification matches images to text prompts using CLIP embeddings, allowing you to assign labels to images without collecting labeled training data or fine-tuning models.

What is the best way to perform cross-modal retrieval for product photos using natural language?▼

Cross-modal retrieval uses CLIP embeddings to find the best matching images for text queries, enabling you to search a folder of product photos using natural language descriptions like "red sneakers".

Can I use PyTorch and Transformers to estimate safe and unsafe categories for content moderation?▼

Yes, multimodal content moderation estimates categories like safe or unsafe by applying prompt-based labels to images using PyTorch-compatible inference with the CLIP model, requiring no fine-tuning.

How does image-text similarity scoring work when matching visuals to descriptive queries?▼

Image-text similarity scoring works by preprocessing images and tokenizing text to produce normalized feature vectors, then calculating cosine similarity to match visuals to descriptive queries.

Do I need to fine-tune a model to run zero-shot classification on a custom set of label prompts?▼

No, zero-shot classification using CLIP embeddings requires no fine-tuning, allowing you to directly compare your images against a custom list of label prompts using pre-trained models.

What dependencies are required to process images and text for cross-modal retrieval?▼

Cross-modal retrieval requires PyTorch, Transformers, and Pillow to handle image preprocessing and text tokenization, producing normalized feature vectors for cosine similarity calculations.