---
name: cloudinary-docs
description: Looks up implementation details in the latest Cloudinary docs via llms.txt. Use when building code or answering questions relating to image or video uploads, optimization, or transformations, and for Cloudinary SDKs, APIs, webhooks, or integrations.
license: MIT
metadata:
  author: cloudinary
  version: '1.0.1'
---

# Cloudinary Documentation

Helps developers integrate Cloudinary into their applications by providing documentation and code examples retrieved directly from the optimized markdown files in the Cloudinary documentation.

## When to Use

- When a user asks questions or requests code implementation relating to image or video upload, management, optimization, or transformations (resizing, applying effects, visual improvements, adding overlays, generative AI, etc.)
- User asks about Cloudinary SDKs, upload APIs, or integration guides
- General Cloudinary documentation lookup (account settings, webhooks, DAM features)
- Looking up specific Cloudinary API endpoints or SDK methods
- Use this skill in conjunction with more specialized Cloudinary skills when relevant.

## Instructions

When answering image and video upload, management, optimization, or transformation questions or when implementing Cloudinary code:

1. **First, get the documentation index** using llms.txt with the llms.txt URL - https://cloudinary.com/documentation/llms.txt
2. **Analyze the llms.txt content** to understand what documentation pages are available
3. **Reflect on the user's question** and identify which specific documentation URLs would be most relevant
4. **Navigate** to the specific relevant documentation URLs from the llms.txt index (you can make multiple calls)
5. **Use the fetched documentation** to provide a comprehensive, accurate answer or code implementation.  When relevant, use in conjunction with more specialized Cloudinary skills like cloudinary-transformations. The best practices defined in the specialized skills should guide which doc instructions to use.

Example workflows:

**Example 1: Upload question**
- User asks: "How do I upload images to Cloudinary?"
- You retrieve the llms.txt index: https://cloudinary.com/documentation/llms.txt
- You analyze the llms.txt content to understand what documentation pages are available
- You identify relevant pages like "image_upload.md" or "upload_api.md"
- You retrieve those specific pages from the llms.txt index
- You provide an answer with code examples and citations

**Example 2: Transformation question**
- User asks: "How do I resize and crop images?"
- You retrieve the llms.txt index
- You identify relevant pages like "image_transformations.md" or "transformation_reference.md"
- You fetch the specific documentation
- You provide transformation syntax and examples

**Example 3: SDK question**
- User asks: "What's the Node.js SDK for Cloudinary?"
- You retrieve the llms.txt index
- You identify SDK-related pages
- You provide installation instructions and usage examples