Best use case
stan-bayesian-modeling is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Stan probabilistic programming for Bayesian inference
Teams using stan-bayesian-modeling 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/stan-bayesian-modeling/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How stan-bayesian-modeling Compares
| Feature / Agent | stan-bayesian-modeling | 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?
Stan probabilistic programming for Bayesian inference
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
# Stan Bayesian Modeling ## Purpose Provides Stan probabilistic programming capabilities for Bayesian inference and statistical modeling. ## Capabilities - Stan model specification - MCMC sampling (NUTS, HMC) - Variational inference - Prior predictive checks - Posterior predictive checks - Model comparison (LOO-CV, WAIC) ## Usage Guidelines 1. **Model Specification**: Write Stan code with clear blocks 2. **Prior Selection**: Choose appropriate, weakly informative priors 3. **Diagnostics**: Check Rhat, ESS, and divergences 4. **Model Comparison**: Use LOO-CV for model selection ## Tools/Libraries - Stan - CmdStan - RStan - PyStan
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