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Build, iterate, and test Bayesian models using PyMC within marimo reactive notebooks, with fast test fixtures and CI/CD-friendly model validation.
What's Inside
Skills3
marimo-notebook
/marimo-notebook
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
pymc-modeling
/pymc-modeling
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers on tasks involving: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, WAIC, model comparison, or causal inference with do/observe.
pymc-testing
/pymc-testing
Testing PyMC models with pytest. Use when writing unit tests for Bayesian models, setting up test fixtures, mocking MCMC sampling, or testing model structure. Covers pymc.testing.mock_sample, pytest fixtures, and the distinction between fast structure-only tests (mocking) and slow posterior inference tests. Triggers on: testing PyMC, pytest, unit tests for models, mock sampling, test fixtures, CI/CD for Bayesian models.
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Version1.0.0
LanguagePython
Stars43
Forks9
MaintenanceExcellent
LicenseMIT
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