clip

Classify images and retrieve cross-modal matches using pretrained CLIP models.

Updated Aug 23, 2026

Search Tags:#multimodal #retrieval #zero-shot #clip #vision-language #image-classification #cross-modal

Ready to run

npx skills add https://github.com/AlexKoncept/omnia-hub --skill clip-alexkoncept

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/AlexKoncept/omnia-hub/tree/main/HERMES/optional-skills/mlops/clip
Command: npx skills add https://github.com/AlexKoncept/omnia-hub --skill clip-alexkoncept

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

CLIP enables machines to understand images and text through a shared embedding space, enabling zero-shot classification and cross-modal retrieval without task-specific fine-tuning.

Core Features & Use Cases

Zero-shot image classification: classify images into defined categories without labeled training data.
Image-text similarity and cross-modal retrieval: find images by text queries or text by image references.
Content moderation and visual search: support safe content filtering and media search workflows.

Quick Start

Run a CLIP workflow to classify or retrieve images by text queries using the preinstalled model.

Frequently Asked Questions about clip

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

FAQPage Schema

How does cross-modal retrieval match text queries to images?▼

Cross-modal retrieval maps images and text into a shared embedding space to measure similarity directly. This Skill exposes a workflow to embed text queries and image references using pretrained CLIP models, enabling image search by text or text by image.

Can I use pretrained CLIP models with Python for content moderation?▼

Yes, pretrained CLIP models support content moderation by classifying images against safety categories zero-shot. This workflow uses transformers, torch, and PIL to embed visual content and compare it against textual moderation criteria.

What is the best way to classify images into custom categories without fine-tuning?▼

Zero-shot classification via pretrained vision-language models allows custom categorization without fine-tuning. This Skill provides a Python workflow using CLIP to embed images and your defined text categories, computing similarity for classification.

Do I need task-specific training data for visual search and image-text similarity tasks?▼

No, task-specific training data is not required for visual search or image-text similarity. CLIP operates zero-shot by leveraging a shared embedding space to match text queries directly with image references across open-world datasets.