# RED REVIEW

- **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:** [Tayfur Ertas](https://cerebralvalley.ai/u/Heraldd)
- **GitHub:** https://github.com/tayfurertas/red_review
- **Demo video:** https://www.youtube.com/watch?v=62zFKwrL9fg
- **Gallery:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/151

RED REVIEW is a forensic audit tool for clinical-AI preprints that produces executable evidence instead of prose critique. Paste an arXiv URL. In under two minutes, it returns a structured red-team report backed by runnable artifacts: a citation whose content does not actually support the paper's claim, a reference domain mismatched between paper and source, a statistical test misused since 2012. 
Three Claude Code agents run in parallel (Citation Forensics, Guideline Auditor, Statistical Critic) unified through a skills-first architecture with CONSORT-AI and TRIPOD+AI authored as progressive-disclosure item files. 
The hero capability is Citation Semantic Verification: Opus 4.7 with extended thinking fetches each cited paper, reads it in full, then checks whether the citation actually supports the claim being made. This inverts the "LLMs hallucinate citations" problem, applied to human authors. On a real published paper, it caught a methodology citation that pointed to the test's implementation paper rather than its origin, and mischaracterized the test as a t-test when it is U-statistics-based.
AI peer review that produces prose critique manufactures confident wrong answers. RED REVIEW produces evidence.

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Markdown version of https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/151. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt