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clip

Classify images and match them to text using CLIP embeddings.

Updated May 4, 2026
Search Tags:#retrieval#zero-shot#clip#vision-language#image-classification#image-search#content-moderation
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill clip-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/clip
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill clip-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you understand images from natural language so you can classify visual content, compare images and text, and search large image collections without building a custom vision model.

Core Features & Use Cases

  • Zero-shot classification: Assign descriptive labels to images without task-specific training.
  • Image-text matching and retrieval: Rank captions, prompts, or assets by semantic similarity.
  • Use cases: Content moderation, semantic image search, deduplication, and lightweight visual question answering.

Quick Start

Use the clip skill to analyze the attached image against a short list of labels and return the most likely match with confidence.

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 custom model?▼

Zero-shot image classification assigns descriptive labels to images without task-specific training by comparing normalized image and text embeddings to find the highest semantic similarity match.

Can I use CLIP for semantic image search and content moderation?▼

CLIP supports semantic image search and content moderation by ranking captions, prompts, or assets based on semantic similarity scores derived from normalized vision-language embeddings.

How do I match images to text prompts for visual question answering?▼

Image-to-text matching for visual question answering processes images and text queries through a preprocessing pipeline to generate normalized embeddings and calculate reliable similarity scores.

What dependencies do I need to run CLIP for image-text retrieval?▼

Running CLIP for image-text retrieval requires a CLIP model environment with torch and pillow support, along with the provided preprocessing pipeline to ensure reliable similarity scoring.

Why does my CLIP similarity scoring return inconsistent results?▼

Inconsistent similarity scoring occurs when image and text embeddings are not properly normalized or when the provided preprocessing pipeline is skipped, degrading zero-shot inference accuracy.

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