pymc-probabilistic-programming

PyMC for flexible Bayesian modeling

509 stars

Best use case

pymc-probabilistic-programming is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

PyMC for flexible Bayesian modeling

Teams using pymc-probabilistic-programming should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/pymc-probabilistic-programming/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/pymc-probabilistic-programming/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/pymc-probabilistic-programming/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How pymc-probabilistic-programming Compares

Feature / Agent	pymc-probabilistic-programming	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

PyMC for flexible Bayesian modeling

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# PyMC Probabilistic Programming

## 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

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

## Tools/Libraries

- PyMC
- ArviZ
- Theano/PyTensor

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