clip

Classify images, search semantically, and moderate content with OpenAI's CLIP model.

Updated May 8, 2026

Search Tags:#semantic search #OpenAI #content moderation #CLIP #image classification

Ready to run

npx skills add https://github.com/superfhp/lumi-agent --skill clip-superfhp

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive solution for image classification, semantic image search, and content moderation using OpenAI's CLIP model, enabling zero-shot image understanding and analysis without fine-tuning.

Core Features & Use Cases

Zero-shot Image Classification: Automatically classify images into predefined categories without training data.
Semantic Image Search: Retrieve similar images based on semantic content.
Content Moderation: Detect and flag inappropriate content in images.
Use Case: For a content moderation platform, use this Skill to automatically identify and flag images containing adult content.

Quick Start

Use the clip skill to classify the image 'sunset.jpg' and print the top 3 categories.

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 categorizes images into predefined classes without training data by using OpenAI's CLIP model to match image features against text labels, returning the most probable categories automatically.

Can I use OpenAI's CLIP for automated content moderation to flag inappropriate images?▼

Yes, you can use the CLIP model for content moderation to detect and flag inappropriate images by evaluating the image against text descriptions of restricted or adult content to determine if it should be flagged.

How do I build a semantic image search to retrieve similar images based on content?▼

Semantic image search uses the CLIP model to embed images and text into the same vector space, allowing you to retrieve visually or contextually similar images by matching their semantic content embeddings.

Do I need PyTorch and transformers to run CLIP for image analysis?▼

Yes, running CLIP for image analysis requires the transformers, torch, and pillow libraries to process image and text data, load the model, and perform the underlying tensor computations for classification.

What is the best way to classify images into custom categories using CLIP?▼

The best way to classify images into custom categories using CLIP is zero-shot classification, which directly compares image embeddings against your custom text category labels without requiring any model fine-tuning.

Are there limitations when using CLIP for visual question answering tasks?▼

Using CLIP for visual question answering is limited by its zero-shot design, meaning it matches image features to text prompts but lacks deep multi-step reasoning required for complex visual question answering scenarios.