How to Reverse-Engineer a Viral Format with OpenClaw
Reverse-Engineer a Viral Format with OpenClaw and ToolRouter. Break down a viral video into its structural components so you can replicate what actually made it work.
Tool
Submit a batch of viral video URLs and OpenClaw will analyze the full set to identify patterns across formats. This is the right approach when building a format library from a large reference set rather than analyzing videos one at a time.
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 Viral Video Clone tool:
- Define the full list of video URLs, the content category, and the output schema before batching.
- Run `viral-video-clone` with `analyze_video` across the full batch.
- Review the batch outputs and identify structural patterns shared across the highest-performing videos.
- Consolidate the findings into a format playbook for your content team.
Example Prompt
Try this with OpenClaw using the Viral Video Clone tool
Use viral-video-clone to analyze these 15 TikTok videos from the personal finance category. For each, return: hook timing, tension mechanism, payoff type, CTA. After all 15 are analyzed, identify the three structural patterns that appear most consistently across the top-performing videos.
Tips
- Analyze a large enough sample — at least 10 videos — before drawing conclusions about format patterns.
- Sort the input videos by view count before batching so the pattern analysis weights high performers appropriately.
- Ask OpenClaw to separate format patterns by content subcategory — what works for educational content may differ from entertainment in the same niche.