clip

Name: clip
Availability: InStock
Author: ldzhhxx

Classify images, match text, and moderate content with CLIP.

1|1|Updated May 9, 2026

Search Tags:#semantic search #AI classification #image processing #content moderation #zero-shot

Ready to run

npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill clip-ldzhhxx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/optional-skills/mlops/clip
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill clip-ldzhhxx

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

CLIP skill addresses the need for efficient image processing by leveraging AI for tasks such as classification, text retrieval, and content moderation.

Core Features & Use Cases

Image Classification: Quickly categorize images without need for training data.
Image-Text Matching: Match images with text descriptions or vice versa.
Semantic Image Search: Retrieve images relevant to a text query.
Content Moderation: Identify potentially harmful or inappropriate content in images.
Use Case: For content creators, the skill can help moderate images in real-time, reducing the manual effort of monitoring and flagging inappropriate content.

Quick Start

To classify an image as 'dog' or 'cat', you can run: python classify_image.py 'dog' 'path/to/image.jpg'

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 allows you to categorize images without training data by passing text labels directly to the script. You can run python classify_image.py 'dog' 'path/to/image.jpg' to instantly process images without manual intervention.

Can I use AI for semantic image search to retrieve relevant photos?▼

Semantic image search retrieves images relevant to a text query by matching text descriptions with image content. This AI-powered algorithm processes visual data to find matching images without requiring manual tagging or metadata.

How does zero-shot content moderation work for identifying inappropriate images?▼

Zero-shot content moderation identifies potentially harmful or inappropriate content in images by evaluating visual data against text descriptions. It reduces the manual effort of monitoring and flagging inappropriate content in real-time for content creators.

Do I need PyTorch and Transformers to run image-text matching tasks?▼

Yes, image-text matching requires PyTorch, Transformers, and Pillow dependencies to process images and match them with text descriptions. These frameworks provide the foundational AI-powered algorithms necessary for zero-shot classification and retrieval.

What is the best way to moderate e-commerce product images automatically?▼

The best way to moderate e-commerce product images automatically is using AI-powered classification algorithms that identify inappropriate content without manual intervention. This zero-shot approach categorizes products and flags harmful content in real-time.

Why does zero-shot image classification work without custom model training?▼

Zero-shot image classification works without custom model training because it leverages pre-trained AI models to match image features against provided text labels. This eliminates the need for manual training data while still processing classification tasks accurately.