How to Extract Styling Insights with OpenClaw
Extract styling insights with OpenClaw and ToolRouter. Decode the styling decisions in any fashion photo to learn what makes the look work.
Tool
Decode the styling decisions in any fashion photo — proportions, colour strategy, layering, and what makes the look work. OpenClaw is the right tool for systematic competitive or trend analysis — extracting insights across a large image set to identify patterns.
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 Outfit Stylist tool:
- Define the analysis schema — techniques to identify, output format — before batching the image set.
- Run `extract_styling` with `outfit-stylist` across the full set with consistent analysis parameters.
- Aggregate the findings and identify techniques that appear in more than 30% of images.
- Export the trend summary as a competitive intelligence or trend research report.
Example Prompt
Try this with OpenClaw using the Outfit Stylist tool
Use outfit-stylist to extract styling insights from these 25 images from 5 competing brands (5 images per brand). For each image, identify the proportion, colour, and layering techniques. Then summarise the most common techniques per brand and highlight where the brands differ most.
Tips
- Define the technique taxonomy before batching — 'tonal dressing', 'volume contrast', 'anchor piece' — so outputs are comparable across images.
- Run one image per brand first to confirm the analysis depth is consistent before committing to the full 25-image batch.
- Summarise by brand rather than by image for competitive analysis — brand-level patterns are more actionable than image-by-image lists.