RepliKit

RepliKit is a web app (with optional desktop agent) that turns messy interdisciplinary methods into runnable, shareable “repro packs.” Researchers upload a paper’s methods, code snippets, datasets links, and environment notes; the app guides them through a structured checklist (stats, wet lab, simulation, survey, etc.) and outputs a standardized package: container recipe, dependency lockfile, data provenance sheet, and a minimal runbook. An AI assistant helps extract steps from PDFs and lab notebooks, but the product is honest about uncertainty: it highlights missing parameters, ambiguous steps, and unverifiable claims instead of pretending it can infer them. Teams can request a “replication ticket,” track what was tried, log deviations, and publish a reproducibility scorecard. The goal is not literature discovery—it’s reducing the friction and embarrassment of “we can’t rerun this” across disciplines where tooling and norms don’t match.