clip

Classifies images and computes image-text cosine similarity without training.

Updated Apr 10, 2026

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

Ready to run

npx skills add https://github.com/KarlinskyS/hermesSkills --skill clip-karlinskys

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/KarlinskyS/hermesSkills/tree/main/mlops/models/clip
Command: npx skills add https://github.com/KarlinskyS/hermesSkills --skill clip-karlinskys

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The CLIP skill provides zero‑shot image understanding, enabling classification, similarity search, and content moderation without any model fine‑tuning.

Core Features & Use Cases

Zero‑shot classification: Identify objects in images using only textual labels.
Image‑text similarity: Compute cosine similarity between images and text for semantic search.
Content moderation: Detect unsafe or violent content in images using predefined categories.
Batch processing & vector DB integration: Efficiently embed large image collections and query them with natural language.

Quick Start

Ask the assistant to analyze 'photo.jpg' and return the most likely label from the list ["a dog","a cat","a bird","a car"].

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 training a model?▼

Zero-shot image classification without training is achieved by encoding images and textual labels using a transformer-based vision-language model, then computing cosine similarity to match images to text. You simply provide an image and a list of textual labels for the model to evaluate.

Can I use CLIP for semantic search across a large batch of images?▼

Yes, you can use CLIP for semantic search across large image collections. It efficiently embeds batched images into vectors that can be stored in a vector DB, allowing you to query the database and retrieve relevant photos using natural language text.

Do I need a GPU runtime to compute image-text similarity?▼

You do not strictly need a GPU runtime to compute image-text similarity, as the model supports both GPU and CPU execution. However, using a GPU is highly recommended for batch processing or embedding large visual datasets efficiently.

What is the best way to moderate unsafe visual content without fine-tuning?▼

The best way to moderate unsafe visual content without fine-tuning is to use zero-shot classification with predefined textual categories. The vision-language model evaluates the image against safety descriptions to detect violent or unsafe material.

When should I not use zero-shot vision-language models for image tasks?▼

You should not use zero-shot vision-language models when you need highly specialized domain accuracy that requires custom training. Because zero-shot classification relies on predefined textual labels and general embeddings, it may underperform on niche visual datasets compared to fine-tuned models.