pymc-labs

Media Mix Modeling with PyMC-Marketing. Use when building MMMs, specifying adstock/saturation transformations, setting priors, fitting multidimensional (geo-level) models, computing channel contributions, ROAS, running budget optimization, calibrating with lift tests, or performing sensitivity analysis. Covers the MMM class, GeometricAdstock, LogisticSaturation, BudgetOptimizerWrapper, and ArviZ diagnostics for marketing models.

PyMC-Marketing coding conventions, preferred implementations, and style guidelines.

Create git commits for changes made during the session.

Create a plan based on document research through an interactive, iterative process.

Structure a research based on the user request. Identify what must change in order to complete the task.

Learn how to work in the current folder (repository)

Create git commits for changes made during the session.

Create an implementation based on a plan.

Choose the appropriate CausalPy experiment class from a causal question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled.

Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.

Review CausalPy pull requests end-to-end by classifying PR type, checking branch freshness, mergeability, remote CI, correctness, security, tests, docs, and maintainer concerns. Use when asked to review a PR, assess a branch before merge, summarize PR risks, or request changes.

Create, evaluate, and triage GitHub issues for CausalPy. Use when filing a bug, proposing an enhancement, analyzing existing issues, or splitting large work into parent-child sub-issues.

Bring a pull request to green by syncing with main, resolving conflicts safely, and fixing failing checks with CausalPy conventions.

Turn issues into PRs, handle commits, and run prek checks consistently.

Perform structured research and turn findings into an implementation plan.

Interactive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.

How to create a dlab decision-pack directory using generate_dpack() from Python code

Complete reference for decision-lab (dlab). Use when the user asks about creating decision-packs, designing data science agents, running sessions, analyzing results, or anything related to dlab CLI, agent architecture, parallel subagents, or decision-pack configuration. Covers the full workflow from scaffolding to analysis.

Design agent system prompts, parallel architectures, and methodological guardrails for data science decision-packs. Use when creating orchestrator, subagent, or parallel agent systems for analytical workflows. Covers anti-fabrication rules, epistemic humility, when to stop, conflict detection, uncertainty reporting, retry protocols, prompt design principles, and the decision-lab runtime mechanics.

Guide a human through creating a dlab decision-pack by asking questions and then calling generate_dpack(). Use this skill whenever the user wants to create, set up, or scaffold a new decision-pack, agent environment, or Docker-sandboxed config for dlab — even if they don't use the word "decision-pack" explicitly. Trigger on phrases like "set up a new agent", "create an environment for X", "I want to run opencode for Y", "scaffold a project", or "make a new config".

Visual language and UX patterns for Textual TUI applications in dlab

A skill

A skill

Guide for using the dhub CLI — the AI skill manager for data science agents. Covers authentication, publishing, installing, running skills, managing API keys, eval reports, and troubleshooting. Use when users ask about dhub commands, skill publishing workflows, or need help with the Decision Hub CLI.

A skill

Guide for creating effective skills for Claude Code agents. Covers skill design, implementation, validation, packaging, and optionally runtime environments and automated evaluations for Decision Hub publishing. Use when users want to create, improve, or package a skill.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A clear description of what this skill does and when to use it. Include specific trigger keywords and task types that should activate this skill. Be specific about capabilities so the AI can decide when to load it. Maximum 1024 characters.