Computational notebooks have become central to data-driven research. But sharing and re-executing them often breaks due to hidden dependencies: R packages, system libraries, compiler toolchains, data paths. Even minor version mismatches or undocumented system requirements can derail reproducibility.
Manually collecting environment details, building Dockerfiles, or relying on package metadata is error-prone and incomplete. And tools that only capture language-level dependencies fall short when notebooks rely on native code or external resources.