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Chris-Chai-Minjae
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clip

Run zero-shot image-text similarity and cross-modal retrieval with CLIP.

Updated Apr 23, 2026
Search Tags:#multimodal#zero-shot#clip#vision-language#image-search#content-moderation#cross-modal
One-click install
npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill clip-chris-chai-minjae
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge/tree/main/optional-skills/mlops/clip
Command: npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill clip-chris-chai-minjae

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

CLIP enables zero-shot image understanding and cross-modal retrieval by connecting vision and language, allowing you to reason about images without task-specific training.

Core Features & Use Cases

  • Cross-modal retrieval (image↔text) and zero-shot classification
  • Image-text similarity scoring for search, moderation, and content curation
  • Flexible deployment with multiple model sizes and preprocessing steps

Quick Start

Load a pre-trained CLIP model and run a simple image-text similarity example.

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 task-specific training?▼

Zero-shot image classification uses a vision-language model to connect images and text, letting you reason about images without task-specific training. It applies image-text similarity scoring to classify diverse image datasets.

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

Cross-modal retrieval matches images with text using a vision-language model. It scores image-text similarity to enable search, content moderation, and semantic similarity workflows across diverse image datasets.

Can I use transformers and torch for image-text similarity scoring?▼

Yes, image-text similarity scoring requires Python with transformers, torch, and pillow to run. It loads a pre-trained CLIP model and applies preprocessing steps for flexible deployment with multiple model sizes.

What's the best way to run content moderation across diverse image datasets?▼

Content moderation across diverse image datasets uses a vision-language model for zero-shot image understanding. It scores image-text similarity to flag inappropriate content without requiring task-specific training.

Does the CLIP model support captioning workflows and multiple model sizes?▼

Yes, the CLIP model supports captioning workflows and flexible deployment with multiple model sizes. It uses preprocessing steps and optional references for extended workflows to process diverse image datasets.

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