A unified interface for analyzing images and performing OCR using state-of-the-art vision models like GPT-4o, Claude, and Gemini.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install oatda-vision-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install oatda-vision-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Oatda Vision Analysis?
Oatda Vision Analysis is a robust technical integration designed to provide AI agents with visual capabilities through a single, streamlined API. By utilizing Openclaw Skills, developers can easily bridge the gap between text-based reasoning and visual understanding, allowing agents to process everything from simple photos to complex technical documentation. This skill abstracts the complexities of different provider requirements, offering a consistent JSON interface for interacting with the world's most powerful vision-capable LLMs.
Built for high-performance automation, the skill supports a wide range of providers including OpenAI, Anthropic, and Google. Whether you need to describe a scene, extract text from a screenshot, or interpret a data chart, this skill ensures that your agent can handle visual inputs with precision. It is an essential component for any developer looking to expand the sensory reach of their AI applications using Openclaw Skills.
Oatda Vision Analysis Use Cases
- Performing high-accuracy OCR on documents and screenshots for data extraction.
- Interpreting technical diagrams, flowcharts, and system architectures for documentation.
- Generating descriptive alt-text and accessibility labels for web imagery.
- Analyzing UI/UX screenshots to identify design elements or potential bugs.
- Processing visual evidence in automated support or reporting workflows.
How Oatda Vision Analysis Works
- The skill detects a request for image analysis and checks for a valid OATDA API key in the environment or configuration files.
- It maps the user's requested model (or default) to the appropriate provider endpoint.
- The skill validates the image source, accepting secure HTTPS URLs or base64 data URIs while rejecting unsafe or local paths.
- It constructs a structured JSON payload using the contents array format required by the unified API.
- The request is sent to the OATDA vision endpoint with specified detail levels (auto, low, or high).
- The skill receives and parses the response, returning the text analysis along with token usage and cost metrics.
Oatda Vision Analysis Setup
To integrate this capability into your project, ensure you have the required binaries and your API key from the OATDA dashboard. These Openclaw Skills require curl and jq for command-line execution.
# Set your API key environment variable
export OATDA_API_KEY="your_api_key_here"
# Or configure via the credentials file
mkdir -p ~/.oatda
echo '{"profiles": {"default": {"apiKey": "your_api_key_here"}}}' > ~/.oatda/credentials.json
Oatda Vision Analysis Data Schema & Taxonomy
The skill utilizes a standardized data structure to maintain consistency across different vision providers. The request and response schemas are detailed below:
| Field | Type | Description |
|---|---|---|
| provider | string | The target AI provider (openai, anthropic, google, etc.) |
| model | string | The specific vision model ID (e.g., gpt-4o, gemini-2.0-flash) |
| contents | array | Array of objects containing text prompts and image data |
| response | string | The final AI-generated description or analysis |
| usage | object | Metrics showing prompt, completion, and total tokens |
| costs | object | Real-time cost calculation in USD |
Oatda Vision Analysis Advanced Features
- Multi-Provider Failover: Easily switch between GPT, Claude, and Gemini models to find the best fit for your specific visual task.
- Granular Detail Control: Use high detail mode specifically for OCR tasks to ensure maximum character recognition accuracy.
- Real-time Cost Tracking: Get immediate visibility into the financial impact of image processing with per-request cost reporting.
- Unified Request Format: Avoid provider-specific breaking changes by using a single, stable JSON schema for all vision requests.
- Automatic Model Mapping: Simplified aliases like sonnet or gpt-4o are automatically resolved to the latest production-ready model IDs.
SKILL.md
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