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Baywright teaching verb (invoke as /baywright:bw-explain <concept>): explain a Bayesian-workflow concept in plain language — what it is, why it matters, how to read it — then point to the current docs for the chosen tool instead of pasting code. For learners. Trigger on: /baywright:bw-explain, "explain <concept>", "what is SBC / R-hat / ELPD / a funnel", "help me understand this diagnostic".
How this skill is triggered — by the user, by Claude, or both
Slash command
/baywright:bw-explain [concept, e.g. SBC, R-hat, divergences, ELPD, non-centering][concept, e.g. SBC, R-hat, divergences, ELPD, non-centering]This skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Explain the requested concept for someone learning Bayesian modeling. Teach, don't lecture.
Explain the requested concept for someone learning Bayesian modeling. Teach, don't lecture.
calibration for SBC and LOO-PIT, computation-and-diagnostics for R-hat
and ESS, reparameterization for funnels, model-comparison for ELPD).Check understanding with a question at the end. Never invent a citation; if a precise reference matters, say to look it up.
npx claudepluginhub 3shn/baywrightGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.