pymc-bayesian-modeler
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
Best use case
pymc-bayesian-modeler is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
Teams using pymc-bayesian-modeler 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pymc-bayesian-modeler/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pymc-bayesian-modeler Compares
| Feature / Agent | pymc-bayesian-modeler | 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 probabilistic programming skill for hierarchical Bayesian models in physics data analysis
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 Bayesian Modeler ## Purpose Provides expert guidance on PyMC for Bayesian modeling in physics, including hierarchical models and advanced inference methods. ## Capabilities - Probabilistic model construction - NUTS/HMC sampling - Variational inference - Gaussian processes - Model comparison (WAIC, LOO) - Prior predictive checks ## Usage Guidelines 1. **Model Building**: Construct probabilistic models 2. **Priors**: Specify informative or weakly informative priors 3. **Sampling**: Use NUTS for efficient sampling 4. **Diagnostics**: Check convergence with trace plots and r-hat 5. **Comparison**: Compare models with information criteria ## Tools/Libraries - PyMC - arviz - Theano/JAX
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