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JamesFincher
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clip

Encodes images and text into a shared embedding space for cosine similarity ranking.

Updated May 4, 2026
Search Tags:#embeddings#multimodal#image search#content moderation#clip#zero-shot classification#cross-modal retrieval
One-click install
npx skills add https://github.com/JamesFincher/gengar --skill clip-jamesfincher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/JamesFincher/gengar/tree/main/optional-skills/mlops/clip
Command: npx skills add https://github.com/JamesFincher/gengar --skill clip-jamesfincher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of matching images to natural-language descriptions without labeled training data, enabling fast semantic search and broad categorization from pixels alone.

Core Features & Use Cases

  • Zero-shot image classification: Assigns images to candidate categories described in text (e.g., “a photo of a dog”) without fine-tuning.
  • Image-text similarity & matching: Computes embedding similarity to rank the best captions or labels for a given image.
  • Cross-modal retrieval and moderation: Finds relevant images from a text query and supports coarse safety/content checks using text category prompts.
  • Use case: Build a search experience for an image library where a user types “a sunset over the ocean” and returns the most semantically similar photos from your collection.

Quick Start

Ask an AI to run CLIP embeddings on your image files and rank them against text prompts like “a sunset over the ocean,” returning the top matching filenames with similarity scores.

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 labeled training data?▼

Semantic image search encodes both image assets and natural-language text queries into normalized embeddings, then ranks images by cosine similarity to the query. This retrieves semantically relevant photos like "a sunset over the ocean" in seconds.

How does cross-modal retrieval work for matching text to images?▼

Semantic image search encodes both image assets and natural-language text queries into normalized embeddings, then ranks images by cosine similarity to the query. This retrieves semantically relevant photos like "a sunset over the ocean" in seconds.

Can I use CLIP embeddings for coarse content moderation across a large image library?▼

Semantic image search encodes both image assets and natural-language text queries into normalized embeddings, then ranks images by cosine similarity to the query. This retrieves semantically relevant photos like "a sunset over the ocean" in seconds.

What is the best way to run image-text similarity matching for semantic search?▼

Semantic image search encodes both image assets and natural-language text queries into normalized embeddings, then ranks images by cosine similarity to the query. This retrieves semantically relevant photos like "a sunset over the ocean" in seconds.

Do I need to fine-tune a model to categorize images using natural-language descriptions?▼

Semantic image search encodes both image assets and natural-language text queries into normalized embeddings, then ranks images by cosine similarity to the query. This retrieves semantically relevant photos like "a sunset over the ocean" in seconds.

Are there limitations when using CLIP for zero-shot classification and image retrieval?▼

Semantic image search encodes both image assets and natural-language text queries into normalized embeddings, then ranks images by cosine similarity to the query. This retrieves semantically relevant photos like "a sunset over the ocean" in seconds.

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