Use this skill when working with bnlearn, a Python library for Bayesian networks, probabilistic graphical models, causal discovery, parameter learning, structure learning, inference, sampling, and directed acyclic graphs (DAG).
Add concise Base.show and Base.summary methods to Julia types whose default REPL representation is unhelpful or overwhelming. Use this skill whenever the user mentions that a type prints badly in the REPL, asks to improve how an object is displayed or printed, wants a custom show, summary, or repr for a Julia type, or…
Add or normalise Julia docstrings on public symbols (exported types, functions, and constants) so the package's public API is fully self-documenting and the Documenter.jl docs build passes its checkdocs check. After writing docstrings, also updates docs/src/API/ pages so every exported symbol appears exactly once…
End-to-end clinical diagnostic reasoning skill. Takes a patient case (SCENARIO.md), runs multi-persona Bayesian reasoning rounds with textbook evidence and literature search, outputs structured DDx with likelihood ratios. Use when solving clinical cases, generating differential diagnoses, discussing clinical…