pymc

Bayesian modeling with PyMC — build hierarchical models, run MCMC with NUTS sampler, perform variational inference, LOO/WAIC model comparison, and posterior predictive checks for probabilistic programming and full Bayesian inference.

Build hierarchical Bayesian models with MCMC and posterior checks

Source K-Dense AI

License MIT

First documented 2026-04-28

Science Data Science

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

Bayesian model with PyMC
hierarchical model
MCMC sampling
posterior distribution
Bayesian inference

What it does

pymc is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a PyMC expert for probabilistic programming — specifying prior distributions, building hierarchical model structures, sampling with the NUTS sampler, running variational inference (ADVI) for faster approximate posteriors, comparing models with LOO/WAIC, and validating fits with posterior predictive checks.

A session produces a complete Bayesian analysis: model specification in PyMC’s model context, MCMC sampling with convergence diagnostics (R-hat, effective sample size), posterior summary statistics, and visualization of the posterior distributions and predictive checks with ArviZ.

When to use it

Reach for it when:

You need full posterior distributions rather than point estimates and confidence intervals
You’re building hierarchical models where partial pooling across groups is theoretically motivated
You want to formally propagate uncertainty through a prediction pipeline using Bayesian updating

When not to reach for it:

Frequentist inference with standard tests — use statistical-analysis or statsmodels
Predictive ML pipelines where interpretability of uncertainty isn’t the goal — use scikit-learn

Install

Copy the SKILL.md from K-Dense AI’s pymc folder into .claude/skills/pymc/ in your project.

Trigger phrases: “Bayesian model with PyMC”, “hierarchical model”, “MCMC sampling”, “posterior distribution”.

What a session looks like

A typical session has three phases:

Model specification. Describe the data generating process — likelihood function, prior beliefs about parameters, and any group structure for hierarchical pooling. Claude writes the PyMC model context with named priors and likelihood.
Sampling and diagnostics. MCMC runs with pm.sample(). Claude checks convergence diagnostics (R-hat < 1.01, bulk-ESS > 400) and warns if chains didn’t mix.
Posterior analysis. ArviZ plots visualize the posterior distributions and trace plots. LOO or WAIC compares models if multiple are specified, and posterior predictive checks verify the model generates data consistent with observations.

Receipts

Where it works well:

Hierarchical models for grouped data (patients nested in hospitals, students nested in schools) where partial pooling outperforms both complete pooling and no pooling
Small sample sizes where prior information meaningfully improves estimates — PyMC makes the prior specification explicit and auditable

Where it backfires:

High-dimensional parameter spaces slow NUTS sampling dramatically; variational inference (ADVI) is faster but approximate
Convergence failures require understanding of model geometry and reparameterization, which is difficult to diagnose without experience in Bayesian workflow

Pattern that works: start with a simple, non-hierarchical model first, verify it samples well and recovers known parameters on simulated data, then add hierarchical structure incrementally.

Source and attribution

Originally authored by K-Dense Inc.. The canonical SKILL.md lives in the pymc folder of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.