Building Custom Image Analysis Skills in OpenClaw with Local Models

A developer documented their process of creating a custom image analysis skill for OpenClaw using entirely free, local tools without API costs.

Setup and Initial Challenges

The developer runs OpenClaw on Windows 11 via Ubuntu WSL with Ollama as the LLM backend. They encountered limitations with the WebUI's image handling - while they created an uploads folder, the system could only read file information but not analyze image content. This led them to explore alternatives beyond paid API solutions (Claude, Gemini, OpenAI) or hardware purchases.

Solution Development

After installing context7mcp, they evaluated local language models and settled on Qwen2.5 VL. Initial attempts with built-in skills faced issues with model name acceptance and Ollama integration. The breakthrough came through systematic testing: sending images to Ollama via API calls, reading responses, and creating both bash and Python scripts to handle the process.

Implementation Details

• Environment: Windows 11 with Ubuntu WSL
• LLM Backend: Ollama
• Selected Model: Qwen2.5 VL
• Integration Method: API calls to Ollama
• Scripts Created: Bash and Python versions

The custom skill registers natively in OpenClaw and can be invoked with commands like "analyse this image" or "take a look at this photo," returning detailed and accurate responses. The developer notes that future improvements with smaller Qwen3/3.5VL models could enhance performance further.

Despite challenges including multiple reinstalls and frustrations with incomplete open-source tools, the developer describes the experience as creating a "self-fixing, self-improving organism" and remains impressed with OpenClaw's potential for custom skill development.