# clip

Use CLIP for zero-shot image classification, image-text matching, and semantic image search without fine-tuning.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 957 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/18-multimodal/clip/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/clip

## What it does

This skill guides work with OpenAI CLIP, a model that links images with natural-language text. It covers zero-shot classification from chosen labels, image-text similarity, semantic retrieval, and category-based checks for visual content.

The examples load `ViT-B/32`, preprocess images, and normalize embeddings before cosine similarity calculations. Image vectors can be stored in Chroma or FAISS for text queries. Code examples use `transformers`, `torch`, and `pillow`.

CLIP fits broad categories and descriptive labels. It is a poor fit for fine details, object counting, or spatial questions because it processes the whole image and does not return bounding boxes.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/clip/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/clip/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/clip/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/clip/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs clip
```

2. **Manually: put the files in place**
- File: `.github/skills/clip/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 18-multimodal/clip
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/clip/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill clip -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/18-multimodal/clip/SKILL.md

## When to use it
- classify an image without labelled training data
- match a photo against text labels
- retrieve images from a natural-language query
- check an image against safety or violence categories
- store image embeddings in Chroma or FAISS

## Example prompts
- Use the clip skill to classify `photo.jpg` as a dog, cat, bird, or car.
- Apply clip to find a sunset over the ocean among `img1.jpg`, `img2.jpg`, and `img3.jpg`.
- Use clip to check `photo.jpg` against safe, unsafe, and violent content labels.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 32624320e910efe9.

BEGIN THIRD-PARTY FILE 32624320e910efe9
````text
---
name: clip
description: OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Multimodal, CLIP, Vision-Language, Zero-Shot, Image Classification, OpenAI, Image Search, Cross-Modal Retrieval, Content Moderation]
dependencies: [transformers, torch, pillow]
---

# CLIP - Contrastive Language-Image Pre-Training

OpenAI's model that understands images from natural language.

## When to use CLIP

**Use when:**
- Zero-shot image classification (no training data needed)
- Image-text similarity/matching
- Semantic image search
- Content moderation (detect NSFW, violence)
- Visual question answering
- Cross-modal retrieval (image→text, text→image)

**Metrics**:
- **25,300+ GitHub stars**
- Trained on 400M image-text pairs
- Matches ResNet-50 on ImageNet (zero-shot)
- MIT License

**Use alternatives instead**:
- **BLIP-2**: Better captioning
- **LLaVA**: Vision-language chat
- **Segment Anything**: Image segmentation

## Quick start

### Installation

```bash
pip install git+https://github.com/openai/CLIP.git
pip install torch torchvision ftfy regex tqdm
```

### Zero-shot classification

```python
import torch
import clip
from PIL import Image

# Load model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

# Load image
image = preprocess(Image.open("photo.jpg")).unsqueeze(0).to(device)

# Define possible labels
text = clip.tokenize(["a dog", "a cat", "a bird", "a car"]).to(device)

# Compute similarity
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)

    # Cosine similarity
    logits_per_image, logits_per_text = model(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

# Print results
labels = ["a dog", "a cat", "a bird", "a car"]
for label, prob in zip(labels, probs[0]):
    print(f"{label}: {prob:.2%}")
```

## Available models

```python
# Models (sorted by size)
models = [
    "RN50",           # ResNet-50
    "RN101",          # ResNet-101
    "ViT-B/32",       # Vision Transformer (recommended)
    "ViT-B/16",       # Better quality, slower
    "ViT-L/14",       # Best quality, slowest
]

model, preprocess = clip.load("ViT-B/32")
```

| Model | Parameters | Speed | Quality |
|-------|------------|-------|---------|
| RN50 | 102M | Fast | Good |
| ViT-B/32 | 151M | Medium | Better |
| ViT-L/14 | 428M | Slow | Best |

## Image-text similarity

```python
# Compute embeddings
image_features = model.encode_image(image)
text_features = model.encode_text(text)

# Normalize
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)

# Cosine similarity
similarity = (image_features @ text_features.T).item()
````
END THIRD-PARTY FILE 32624320e910efe9

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