Litmus reads a scientific paper and tells you how much to trust it before you build on it. You give it a DOI, some pasted text, or a PDF, and it breaks the paper into its core claims, runs real statistical forensic checks in code (statcheck, GRIM, SPRITE, power, and a p-curve), searches the wider literature for work that supports or contradicts each claim, and then has Claude Opus weigh all of it into a calibrated replication likelihood. Every reason it gives is clickable straight back to the sentence it came from, and anything it can't ground, it throws away. I started with preclinical cancer, where only 6 of 53 landmark studies famously reproduced, because that's where a wrong call costs the most: a bad target isn't caught until Phase II or III, years and hundreds of millions of dollars later. On a set of real papers with known outcomes, Litmus cleanly separates retracted, failed, and robust work, and it stays honest about its limits with confidence intervals. It also ships as an MCP server, so any AI agent can call it to check a result before trusting it. The goal is to make it the verification layer every AI scientist and human runs through.
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