PyMC Labs
Official@pymc-labs
The Bayesian Consultancy
Agent Skills by PyMC Labs
Showing 21 vetted skills indexed across 3 GitHub repositories.
pymc-labsCreate decision-pack Interactively
Guide users through interactive questions to create a dlab decision-pack configuration.
pymc-labsDesign data science agent systems
Design data science agent systems with anti-fabrication protocols and retry mechanisms.
pymc-labsCreate decision-pack Programmatically
Generate decision-packs for data science agents via Python code.
pymc-labsAnalyze dlab session runs
Analyze dlab session logs and outputs for convergence, consistency, and errors.
pymc-labsTUI Design System
Standardize themes, layouts, typography, and color palettes for Textual TUI applications.
pymc-labsopencode
Generate code snippets and analyze code for issues using Python and AI models.
pymc-labsInformative Priors for MMM
Set informative priors for PyMC-Marketing MMM models using domain knowledge.
pymc-labsPyMC Samplers
Configure and troubleshoot PyMC sampling methods with NUTS, Metropolis, and Hamiltonian samplers.
pymc-labsPyMC Data Handling
Register and update PyMC data containers using pm.Data and pm.Minibatch.
pymc-labsPyMC Distributions
Manage, debug, and apply PyMC distributions across PyMC3 and latest versions.
pymc-labsPyMC-Marketing MMM
Fit Bayesian Generalized Additive Models for Marketing Mix Modeling with PyMC-Marketing.
pymc-labsevent-forecasting
Generate probabilistic forecasts for future events using Bayesian models and survival analysis.
pymc-labsdlab-cli
Execute parallel agent analyses with decision-packs in Docker environments.
pymc-labsmodel-evaluation
Compare Bayesian models with ArviZ 1.1 LOO, ELPD, and Bayes factors.
pymc-labsprior-elicitation
Find constrained priors for Bayesian models using PyMC and PreliZ.
pymc-labspymc-testing
Mock PyMC model sampling and provide pytest fixtures for testing.
pymc-labsmarimo-notebook
Run Python notebooks with reactive cell execution on dependencies.
pymc-labspymc-extras
Add B-spline basis functions and distributional regression to PyMC models.
pymc-labsskill-name
Scaffold new Agent Skills with standardized SKILL.md metadata and README templates.
pymc-labspymc-modeling
Guide Bayesian modeling with PyMC v5 and diagnose convergence using ArviZ.
pymc-labsmarimo-notebooks
Create and manage marimo reactive notebooks as Python files with CLI tooling.
Frequently Asked Questions About PyMC Labs
FAQPage SchemaWhat specific analytical tasks can I perform with these capabilities?▼
You can execute Bayesian Marketing Mix Modeling, perform probabilistic event forecasting, elicit constrained priors for complex models, and conduct rigorous model comparison using LOO, ELPD, and Bayes factors.
Who is the target audience for these Bayesian modeling services?▼
These capabilities are designed for data scientists, quantitative researchers, and marketing analysts who require high-precision probabilistic modeling, convergence diagnostics, and robust statistical inference for enterprise decision-making.
What are the primary dependencies for running these models?▼
Core operations require PyMC v5 for probabilistic programming, ArviZ for model evaluation, and PreliZ for prior elicitation. Environments typically utilize Docker for parallel execution and reactive notebook interfaces for cell dependency management.