How to Extract Style from Inspiration Images with OpenClaw
Extract style from inspiration images with OpenClaw and ToolRouter. Build comprehensive style libraries from large image collections.
Tool
OpenClaw handles style extraction at scale — processing a large mood board archive, a competitor's content library, or a full brand shoot's worth of images to extract and organize the visual patterns across all of them.
Connect ToolRouter to OpenClaw
1Install the CLI
npm install -g toolrouter-mcp2Call tools directly from OpenClaw
toolrouter-mcp call web-search search --query "AI tools"
toolrouter-mcp toolsSteps
Once connected (see setup above), use the Image Style Extractor tool:
- Define the full image set and the output format — individual style prompts, grouped by visual category, or synthesized into style families.
- Run extract_style across the full image collection.
- Group the extracted prompts by visual similarity or style family.
- Export the organized style library as a structured reference document.
Example Prompt
Try this with OpenClaw using the Image Style Extractor tool
Use image-style-extractor to extract style prompts from all 40 images in our stock photo library selection. Group similar styles into visual clusters, name each cluster, and return the library so I can identify the 3-4 dominant visual styles we default to.
Tips
- Group by visual style rather than subject — 'warm lifestyle' and 'clean product' are more useful categories than 'people' and 'objects'.
- Identify the dominant style clusters before synthesizing master prompts — the most common styles are the ones worth standardizing.
- Run a second extraction pass on the largest cluster to get a more refined prompt from that style family.