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Grok+17 agentsVision Language Clip
Use OpenAI's CLIP model in Python for zero-shot image classification by text labels, image-text similarity scoring, text-to-image retrieval, and embedding-based
$5
Vision Language Clip
Example session with this skill installed
I have a folder of product photos. Write a script to classify them as 'electronics', 'apparel', or 'furniture' using CLIP and print the results.
- Read your context and instructions
- Compiled the vision language clip
inputs = processor(text=["electronics", "apparel", "furniture"], images=imgs, return_tensors="pt", padding=True)
outputs = model(**inputs)
probs = outputs.logits_per_image.softmax(dim=1)
# Results: img1.jpg -> apparel (0.92), img2.jpg -> electronics (0.88)
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What you get
About this skill
The problem
Training custom computer vision models is slow, requires massive labeled datasets, and fails when new categories are introduced. Developers often need to classify or search images based on natural language descriptions without the overhead of fine-tuning.
What it does
- Performs zero-shot image classification using arbitrary text labels.
- Calculates similarity scores between text captions and image assets.
- Enables text-to-image retrieval by mapping embeddings to a shared latent space.
- Supports image clustering by grouping semantic embeddings.
- Provides a pipeline for batch processing images using GPU acceleration.
Frameworks & tools
Python, PyTorch, Hugging Face Transformers, OpenCLIP, and scikit-learn for clustering.
Why this beats prompting it yourself
While generic LLMs can describe images, they are expensive and slow for batch processing. This skill provides the specific implementation logic for CLIP, handling tensor normalization, logit scaling, and vector operations required for high-performance local execution.
Use cases
- Build a search engine that finds photos based on descriptive text queries.
- Automate image tagging for large e-commerce catalogs without manual labeling.
- Cluster UI screenshots or design assets by visual style and composition.
- Filter user-generated content against specific semantic categories.
Known limitations
Not suitable for fine-grained counting or OCR tasks. Performance is lower than fine-tuned models on fixed taxonomies with thousands of examples.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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