Verified:GitHub

clip

Name: clip
Availability: InStock
Author: zerwiz

Classifies images and retrieves cross-modal matches using natural-language prompts and cosine similarity scoring without training data.

1|Updated Apr 10, 2026

Search Tags:#semantic search #content moderation #zero-shot #clip #vision-language #image classification #cross-modal retrieval

Ready to run

npx skills add https://github.com/zerwiz/wayofpi --skill clip-zerwiz

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the need to train a vision model for every new image task by using natural-language prompts to classify, search, and compare visual content.

Core Features & Use Cases

Zero-shot image classification: Assign labels to images without creating a custom dataset.
Semantic image search: Find relevant images from text queries or retrieve captions for an image.
Content moderation and retrieval: Screen visual content, detect likely categories, and match image-text pairs in production workflows.
Use Case: A team can quickly sort a large folder of product photos into categories like outdoor, indoor, or lifestyle by describing the desired labels in plain language.

Quick Start

Use the clip skill to analyze the attached image against a small set of natural-language labels and return the most likely match.

Frequently Asked Questions about clip

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

FAQPage Schema

How do I classify images with natural-language labels without training data?▼

Semantic image search uses a CLIP model to retrieve cross-modal matches by comparing normalized embeddings of tokenized text queries and preprocessed images, scoring image-text pairs via cosine similarity to find relevant visual content.

Can I use zero-shot image classification for sorting product photos into categories?▼

Yes, zero-shot image classification sorts product photos into categories like outdoor or indoor by describing desired labels in plain language, eliminating the need to train a custom vision model for every new image task.

What do I need for cross-modal retrieval and image-to-text matching?▼

Cross-modal retrieval requires a CLIP model with image preprocessing and tokenized text prompts to generate normalized embeddings, which are then compared using cosine similarity scoring to match image-text pairs accurately.

Does zero-shot image classification work for content moderation and visual question answering?▼

Zero-shot image classification works for content moderation and visual question answering by screening visual content and detecting likely categories using natural-language prompts, matching image-text pairs in production workflows without custom training.

What are the limitations of using CLIP for zero-shot image classification?▼

Limitations of using CLIP for zero-shot image classification include dependence on the quality of natural-language prompts and the necessity of normalized embeddings for accurate cosine similarity scoring across image-text pairs without task-specific training data.