Choxos Bayesianagent
- Updated December 11, 2025
- choxos/BayesianAgent
choxos-BayesianAgent is a Claude Code plugin that helps create and review Bayesian model specifications for Stan, PyMC, JAGS, and WinBUGS.
About
choxos-BayesianAgent is a Claude Code plugin aimed at researchers and data developers who work in more than one Bayesian stack. Instead of asking a generic coding agent for PyMC one day and Stan the next with inconsistent priors and parameterization, you get an ecosystem tuned to creating and reviewing model specification files across cmdstanr, PyMC, R2jags, and R2WinBUGS. That maps cleanly to Validate when you are scoping an analysis—locking likelihoods, priors, and identifiability before expensive sampling—and spills into Idea research when you compare frameworks and into Build when you materialize scripts. It is advanced relative to beginner stats tooling: you should already understand MCMC, convergence, and when JAGS versus Stan matters. The plugin is not a hosted inference service; it is agent assistance for specification quality. Quantitative catalog metadata lists one plugin in the bundle; depth comes from the described create-and-review agent roles rather than a long skills.sh install leaderboard.
- Agent ecosystem focused on create and review loops for Bayesian model specification
- Supports Stan via cmdstanr, PyMC in Python, JAGS via R2jags, and WinBUGS via R2WinBUGS
- Covers regression, hierarchical, survival, time-series, and meta-analysis style workflows
- Emphasizes probabilistic programming and MCMC-ready model code before sampling
- Pairs specification authoring with validation-oriented review for multi-backend projects
Choxos Bayesianagent by the numbers
- Data as of Jul 7, 2026 (Skillselion catalog sync)
/plugin install choxos-bayesianagent@choxos/BayesianAgentAdd your badge
Show developers this plugin is listed on Skillselion. Paste this into your README.
| Last updated | December 11, 2025 |
|---|---|
| Repository | choxos/BayesianAgent ↗ |
What it does
Generate and peer-review Bayesian model files across Stan, PyMC, JAGS, and WinBUGS without switching mental models between ecosystems.
Who is it for?
Best when you're running Bayesian regression, hierarchical, survival, or meta-analysis models and want agent-guided spec and review across R and Python stacks.
Skip if: Skip if you only need frequentist dashboards or and want turnkey cloud MCMC without touching model code.
What you get
You leave each session with reviewed, backend-appropriate model specification files and clearer validation notes before committing to sampling and reporting.
- Draft Bayesian model specification files for the selected backend
- Structured review feedback on priors, parameterization, and model structure
By the numbers
- Plugin count: 1
- Backends enumerated: Stan (cmdstanr), PyMC, JAGS (R2jags), WinBUGS (R2WinBUGS)
- Repository: choxos/BayesianAgent
Recommended Plugins
0xbigboss Linear CliLinear CLI built with Zig1
15195999826 LomomarketplaceLomo's Claude Code Plugin Marketplace1
1broseidon MarketplaceCurated collection of Claude Code plugins for fullstack development teams1
4rgon4ut Cc Zig LspZig LSP plugin for Claude Code4
708u TwigSimplify git worktree and branch management with configurable symlinks, change carrying, and cleanup.22
8avalon8 AvalonclaudemarketAvalon 的 Claude Code 插件市场How it compares
Specialized Bayesian spec-and-review agent plugin, not a general MCP integration or biostatistics trial operations suite.
FAQ
Who is choxos-BayesianAgent for?
It is for analysts and developers who write Bayesian models in Stan, PyMC, JAGS, or WinBUGS and want Claude-assisted creation and review of those specification files.
When should I use choxos-BayesianAgent?
Use it when scoping or validating a study design—before long MCMC runs—to draft models and catch specification issues across your chosen probabilistic programming backend.
How do I add choxos-BayesianAgent to my agent?
Install the plugin from the choxos/BayesianAgent repository in Claude Code and invoke its agents when you need model creation or review workflows for your target stack.