# sc

- **Event:** [Built with Opus 4.6: a Claude Code hackathon](https://cerebralvalley.ai/e/claude-code-hackathon)
- **When:** Feb 10 at 12:00 PM – Feb 17 at 10:00 AM (EST)
- **Where:** Location TBA
- **Team:** [Sahil Chawla](https://cerebralvalley.ai/u/souptaco)
- **GitHub:** https://github.com/csahil/wildcatter
- **Demo video:** https://youtu.be/AgPeJdkai-g
- **Gallery:** https://cerebralvalley.ai/e/claude-code-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/claude-code-hackathon/hackathon/gallery/163

wildcatter turns a natural-language question into a standalone Python script  
  that answers it.                                          
                                                                               
  You ask something like "revenue per comma in the CEO letter for every Fortune
   50 company" — an LLM decomposes it into a plan, writes a Python script that 
  fetches real data (SEC filings, FRED, Wikipedia, Yahoo Finance, web
  scraping), runs it, validates the output with auto-generated tests, and saves
   the result as a reusable "rig."

  Rigs re-run without the LLM. If something breaks (API changed, site
  restructured), the system calls the LLM to fix the code automatically. You
  can also set up triggers to re-run on a schedule or when a condition fires.
  The output is a structured table you can export directly to excel or via MCP

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Markdown version of https://cerebralvalley.ai/e/claude-code-hackathon/hackathon/gallery/163. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt