clip

Classify images and match visual content to text labels.

3|Updated Apr 4, 2026

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

Ready to run

npx skills add https://github.com/twjarviszyra-web/hermes-unbound --skill clip-twjarviszyra-web

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/twjarviszyra-web/hermes-unbound/tree/main/skills/mlops/models/clip
Command: npx skills add https://github.com/twjarviszyra-web/hermes-unbound --skill clip-twjarviszyra-web

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you understand and organize images without training a custom model, making it easy to classify visual content, find matching images, and compare pictures to text descriptions.

Core Features & Use Cases

Zero-shot image classification: Assign likely labels to an image using natural-language categories.
Cross-modal search and retrieval: Match text queries to images or find captions that best describe an image.
Moderation and similarity workflows: Support content screening, duplicate detection, and vector database indexing for large image collections.
Use Case: If you have a folder of product photos, you can quickly sort them by category, search them with phrases like “sunset over ocean,” and identify near-duplicate images.

Quick Start

Use the clip skill to classify the attached image with a short list of 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 text labels without training a custom model?▼

Zero-shot image classification assigns natural-language labels to visual content without custom training. You provide an image and candidate text categories, then the system scores matches using cosine similarity between normalized image and text embeddings.

Can I search a large folder of product photos using natural language queries?▼

Semantic image search matches text queries like “sunset over ocean” to images. It requires text tokenization and image preprocessing to generate embeddings, with optional vector database integration for cross-modal ranking across large image collections.

What is cross-modal image-text retrieval and how does it work?▼

Cross-modal retrieval matches text queries to images or finds captions describing an image. It computes cosine similarity scores between normalized vision-language embeddings, enabling bidirectional search across visual and textual content.

Does zero-shot image classification require a vector database for duplicate detection?▼

Duplicate detection workflows benefit from optional vector database integration to index and query large image collections efficiently. For smaller datasets, direct embedding normalization and cosine similarity scoring can identify near-duplicate images without a vector database.

Can I use this for content moderation and visual question answering?▼

Content moderation and visual question answering are supported workflows. By scoring image-text similarity, the system screens visual content against moderation labels and matches images to descriptive text queries for answering visual questions.

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

Zero-shot classification depends on the quality of provided text labels and image preprocessing. Highly specialized or domain-specific visual categories may yield lower accuracy without carefully chosen natural-language candidate labels.