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pymc-modeling
PyMC 6+ / PyTensor 3+ / ArviZ 1.0+ Bayesian modeling skills, agents, hooks, and tools for Claude Code
What's Inside
Slash Commands4
model-compare
/model-compare
Compare multiple Bayesian models using LOO-CV
prior-check
/prior-check
Generate and analyze prior predictive checks for the current model
pymc-diagnose
/pymc-diagnose
Run full MCMC diagnostics on a DataTree file
shape-check
/shape-check
Validate model shapes and dimensions before sampling
Agents3
diagnostics-agent
/diagnostics-agent
Analyze MCMC diagnostics from ArviZ output, identify convergence issues, and suggest fixes
model-review-agent
/model-review-agent
Review PyMC model code before sampling to catch common mistakes and suggest improvements
prior-elicitation-agent
/prior-elicitation-agent
Help users choose appropriate priors through interactive dialogue and prior predictive checking
Skills5
model-evaluation
/model-evaluation
Load when the user is comparing Bayesian models, computing LOO-CV / ELPD, calling az.loo or az.compare, doing model stacking/averaging, or computing Bayes factors. Covers the ArviZ 1.0 LOO/ELPD/stacking APIs exclusively (no waic). Triggers include: model comparison, LOO, ELPD, az.compare, az.loo, loo_expectations, loo_metrics, loo_r2, Pareto k, stacking, Bayes factor, cross-validation, predictive accuracy, information criterion.
prior-elicitation
/prior-elicitation
Load when the user is choosing priors, running prior predictive checks, calling find_constrained_prior, using PreliZ, or otherwise eliciting domain knowledge into a Bayesian model. Covers weakly informative priors, constrained priors, sensitivity analysis, and elicitation workflows. Triggers include: prior selection, elicitation, find_constrained_prior, PreliZ, prior predictive, expert/informative priors, weakly informative priors, constrained priors.
pymc-extras
/pymc-extras
Load when the user is working with pymc-extras (pmx) features: splines / BSplineBasis, distributional regression / GAMLSS, R2D2M2CP or horseshoe priors, discrete variable marginalization, or Laplace approximation via fit_laplace. Triggers include: pymc_extras, pymc-extras, pmx, splines, BSplineBasis, distributional regression, GAMLSS, R2D2, horseshoe (regularized/Finnish), marginalize, fit_laplace, penalized splines.
pymc-modeling
/pymc-modeling
Load whenever the user is working on code that imports pymc, pytensor, or arviz, or asks about Bayesian modeling, MCMC, priors, posteriors, sampling, or model diagnostics. Covers PyMC 6+, PyTensor 3+, ArviZ 1.0+ (DataTree API), pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Use for building probabilistic models, specifying priors, running MCMC, diagnosing convergence, or comparing models. Triggers include: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, HSGP, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, model comparison, causal inference with do/observe, and any PyTensor Op or graph work.
pymc-testing
/pymc-testing
Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mock_sample, pytest fixtures for Bayesian models, and the distinction between fast structure-only tests (mocking) and slow posterior inference tests. Triggers include: testing PyMC, pytest with pymc, unit tests for Bayesian models, mock sampling, test fixtures for models, CI/CD for PyMC.
MCP Servers1
Stats
Version2.0.0
LanguagePython
Stars3
MaintenanceExcellent
LicenseMIT
Last Commit
Added
Safety Signals
Critical
Admin access level
Server config contains admin-level keywords
Caution
Modifies files
Hook triggers on file write and edit operations
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