clip

Name: clip
Availability: InStock
Author: HUMANERD

Classify images and match them to text using OpenAI's CLIP model.

2|7|Updated Jun 19, 2026

Search Tags:#OpenAI #image search #CLIP #vision-language #image classification

Ready to run

npx skills add https://github.com/humanerd-drew/opencode-drewgent --skill clip-humanerd-drew

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/humanerd-drew/opencode-drewgent/tree/main/skills/mlops/models/clip
Command: npx skills add https://github.com/humanerd-drew/opencode-drewgent --skill clip-humanerd-drew

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of understanding and interacting with images through natural language, enabling tasks like image classification, image-text matching, and semantic image search without the need for fine-tuning.

Core Features & Use Cases

Zero-shot Image Classification: Classify images without training data.
Image-Text Matching: Match images to text descriptions.
Semantic Image Search: Search for images based on text queries.
Content Moderation: Detect inappropriate content in images.
Use Case: For a content moderation platform, this Skill can automatically flag images containing unsafe content, saving time and resources.

Quick Start

Use the clip skill to classify the image 'example.jpg' and get the top 3 probabilities.

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 providing training data?▼

Zero-shot image classification without training data is achieved by using OpenAI's CLIP model to match image content directly against text descriptions. This Skill applies the CLIP model to calculate probabilities and identify image content without fine-tuning.

Can I use Python and PyTorch to build a semantic image search engine?▼

Yes, you can build a semantic image search engine using Python and PyTorch with this Skill. It leverages OpenAI's CLIP model for cross-modal retrieval, enabling you to search for images based on text queries through image-text understanding.

Does OpenAI's CLIP model work for automated content moderation in images?▼

OpenAI's CLIP model works for automated content moderation by matching images against text descriptions of unsafe content. This Skill enables platforms to automatically flag inappropriate images without needing manually fine-tuned classifiers.

What Python libraries do I need to run CLIP for image-text matching?▼

To run CLIP for image-text matching, you need the transformers, torch, and pillow Python libraries. These dependencies provide the deep learning framework and image processing capabilities required to execute the model and process image inputs.

What is the best way to match image content to text descriptions without fine-tuning?▼

The best way to match image content to text descriptions without fine-tuning is applying a zero-shot cross-modal model like CLIP. This Skill processes images and text simultaneously to evaluate semantic similarity directly, skipping the training phase entirely.