# Skim Intelligence

- **Event:** [Built with Opus 4.7: a Claude Code hackathon](https://cerebralvalley.ai/e/built-with-4-7-hackathon)
- **When:** Apr 21 at 12:00 PM – Apr 27 at 2:00 AM (EDT)
- **Where:** Online
- **Team:** [Ojay Shaq](https://cerebralvalley.ai/u/ojay)
- **GitHub:** https://github.com/ojaydev/skim-intelligence
- **Demo video:** https://youtu.be/YaIPCAKgAr4
- **Gallery:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/47

Prediction markets pay bots to exist. Maker rebates, liquidity rewards,
  and mint/burn arbitrage are structural edges that pay regardless of
  which way a market resolves. The catch: surfacing those edges takes
  quant analysis most operators can't afford, so most leave the yield on
  the table.

  Skim Intelligence closes that gap with reasoning, not rules. A
  five-agent pipeline — three of them powered by Claude Opus 4.7 —
  autonomously scans markets across Polymarket and Bayse, reasons about
  each one across three strategy layers (mint/burn arbitrage, market
  making, reward farming), and paper-trades the structurally
  direction-neutral edges.

  The Alpha Agent streams its tool-use JSON token-by-token to a live
  dashboard, so you watch Opus 4.7 walk through the orderbook in real
  time before it commits to a recommendation. The Risk Agent runs an
  independent prompt as a circuit breaker. Execution simulates fills
  with realistic slippage and hard negative-EV guards. Every five
  minutes, Reporter writes an honest P&L attribution.

  Built on the Anthropic Messages API (tool use + streaming + ephemeral
  prompt caching), Cloudflare Workers + Durable Objects + D1, and a
  small Node relay for venue data. Fully open source under MIT — agent
  prompts, paper-trading engine, and orchestration all in the public
  repo.

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Markdown version of https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/47. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt