pymc-bayesian-modeler
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
Profile →npx skills add a5c-ai/babysitter --skill pymc-bayesian-modeler --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the pymc-bayesian-modeler skill do?
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill pymc-bayesian-modeler --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.