clip

Name: clip
Availability: InStock
Author: rnben

Classify images and rank text prompts with a pretrained CLIP model.

1|Updated Apr 18, 2026

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

Ready to run

npx skills add https://github.com/rnben/hermes-skills --skill clip-rnben

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CLIP bridges images and text by computing aligned embeddings to enable zero-shot image classification, image-text similarity, and cross-modal retrieval without task-specific fine-tuning.

Core Features & Use Cases

Zero-shot image classification and image-text similarity
Cross-modal retrieval and image search
Content moderation, visual search, and multimodal QA

Quick Start

Install the CLIP package and run a minimal example to classify an image with a few text prompts.

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 data?▼

Zero-shot image classification is achieved by computing aligned embeddings with a pretrained CLIP model to measure image-text compatibility. This allows categorizing images against custom text prompts without requiring task-specific fine-tuning.

Can I use CLIP for cross-modal retrieval and image search?▼

Yes, CLIP supports cross-modal retrieval and image search by ranking text prompts based on their semantic similarity to image embeddings. This bridges vision and language to find matching content across different data modalities.

What dependencies do I need to compute image-text similarity with CLIP?▼

Computing image-text similarity with CLIP requires installing transformers, torch, and pillow. These libraries handle the pretrained model inference, tensor operations, and image loading required for cross-modal embeddings.

How does CLIP handle multimodal QA and content moderation?▼

CLIP handles multimodal QA and content moderation by ranking text prompts against image embeddings to measure compatibility. This zero-shot approach allows filtering visual content or answering queries without needing labeled training data.

Are there limitations to using pretrained CLIP models for image classification?▼

Pretrained CLIP models perform zero-shot image classification by measuring image-text compatibility, but rely on generic pretraining without task-specific fine-tuning. This limits accuracy on highly specialized domains or nuanced visual concepts not covered in pretraining.