About AI agent tooling for Python data science workflows
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
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…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when the user is comparing Bayesian models, computing LOO-CV / ELPD, calling arvizstats.loo or arvizstats.compare, doing model stacking/averaging, or computing Bayes factors. Covers the ArviZ 1.1 LOO/ELPD/stacking APIs exclusively (no waic). Triggers include: model comparison, LOO, ELPD, compare, looexpectations…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when the user is choosing priors, running prior predictive checks, calling findconstrainedprior, 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…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
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 fitlaplace. Triggers include: pymcextras, pymc-extras, pmx, splines, BSplineBasis, distributional…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
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 1.1+ (DataTree API), pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Use for building probabilistic…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mocksample, 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…