pymc

combined

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference. Builds and checks Bayesian models with PyMC, including hierarchical models, NUTS MCMC, variational inference, mutable-data predictions, posterior predictive checks, diagnostics, and PSIS-LOO model comparison. Use for probabilistic modeling and uncertainty inference in PyMC.

When to use

Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.)
Performing MCMC sampling or variational inference
Conducting prior/posterior predictive checks
Diagnosing sampling issues (divergences, convergence, ESS)
Comparing multiple models using information criteria (LOO, WAIC)
Implementing uncertainty quantification through Bayesian methods
Working with hierarchical/multilevel data structures
Handling missing data or measurement error in a principled way

Skill page

Conflict

The K-Dense-AI copy is a highly specific, version-locked (PyMC 6.3.2) procedure that includes critical modern API details (ArviZ 1.3 changes, `nuts_sampler` vs `nuts_sampler_kwargs`, `PlotCollection` handling) and a rigorous scientific workflow (prior sensitivity, parameter recovery, estimand definition). The other two copies (Vibe-Skills and OpenClaw) are nearly identical to each other, targeting a generic 'PyMC 5.x+' API with standard code snippets and a simpler 8-step workflow. They lack the version-specific constraints and the deeper diagnostic requirements of the K-Dense copy. However, the Vibe/OpenClaw copies provide explicit code templates for common patterns (Linear/Logistic Regression) and a 'Routing Boundary' section that the K-Dense copy omits. Combining them allows for a skill that is both version-aware and practically templated.