PyMC Probabilistic Programming

SkillDev tools

PyMC for flexible Bayesian modeling

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the PyMC Probabilistic Programming skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/mathematics/skills/pymc-probabilistic-programming/SKILL.md and read by ahel’s review.

Purpose

Provides PyMC capabilities for flexible Bayesian modeling and probabilistic programming in Python.

Capabilities

Hierarchical model specification
Custom distributions
Gaussian processes
MCMC and variational inference
Model diagnostics
ArviZ integration for visualization

Usage Guidelines

Model Building: Use PyMC context managers
Custom Distributions: Define distributions when needed
Hierarchical Models: Build proper hierarchical structures
Visualization: Use ArviZ for diagnostic plots

Tools/Libraries

PyMC
ArviZ
Theano/PyTensor

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: pymc-probabilistic-programming
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter