Bayesian Workflow

Bayesian Statistician Plugin

A Claude Code plugin for end-to-end Bayesian statistical modeling with Stan and ArviZ. It packages an orchestrator skill, eleven specialized subagents, and a library of modeling skills that together run a full Bayesian workflow: EDA → model design → fitting → validation → reporting.

Prerequisites

• uv — used for all Python execution.
• A C++ toolchain — CmdStanPy compiles Stan models against CmdStan. The /bayesian-workflow:setup command installs CmdStan via python -m cmdstanpy.install_cmdstan.

Install

From the marketplace (the repository is also its own marketplace):

/plugin marketplace add sunxd3/bayesian-statistician-plugin
/plugin install bayesian-workflow@sunxd3-plugins

For local development, clone the repository and load it with --plugin-dir:

git clone https://github.com/sunxd3/bayesian-statistician-plugin.git
claude --plugin-dir ./bayesian-statistician-plugin

Run /reload-plugins after editing a locally loaded plugin.

Usage

Bootstrap the Python environment once per project (optional — the orchestrator will do it on first run if you skip):

> /bayesian-workflow:setup

Then run the workflow with your data path or analysis goal:

> /bayesian-workflow:run Analyze data/sales.csv and build a Bayesian model

The orchestrator drives the workflow through four phases, delegating to subagents and writing all results into a predictable folder structure (eda/, design/, experiments/, final_report.html, log.md).

Individual phases can also be run standalone — for example, EDA alone:

> /bayesian-workflow:eda data/sales.csv

What's inside

Orchestration skill — orchestration holds the full workflow protocol (phases, task-pool semantics, canonical file structure, dispatch logic). Loaded by the /bayesian-workflow:run command.

Subagents (11) — eda-analyst, analysis-planner, model-designer, prior-predictive-checker, fake-data-checker, model-fitter, posterior-predictive-checker, critique, model-refiner, model-selector, report-writer.

Commands:

• /bayesian-workflow:setup — bootstraps the Python environment (copies shared_utils, creates pyproject.toml, runs uv sync and cmdstanpy.install_cmdstan).
• /bayesian-workflow:run [data-path-or-analysis-goal] — end-to-end pipeline. Loads the orchestration skill and drives all four phases.
• /bayesian-workflow:eda <data_path> [output_dir] [--focus=<area>] — run EDA on a dataset standalone, without the full workflow pipeline. Wraps the eda-analyst subagent.

Modeling skills (13) — validation-protocol, python-environment, stan (with references/ode.md and references/horseshoe.md for ODE-based dynamics and sparse regression), generative-model-design (lean SKILL.md index + references/ for spec sections, design principles, and the resolution-sequence pattern), fake-data-simulation (with references/single-draw.md, references/sbc.md, references/decision.md), convergence-diagnostics, inferencedata-handling, visual-predictive-checks, bayesian-model-diagnostics, bayesian-model-selection, model-critique (SKILL.md index + references/ for statistical, domain, and framework assessment plus verdict templates), eda (with references/process/ for EDA procedures and references/tests/ for a diagnostic test library by data shape), artifact-guidelines (with references/html-report.md, references/markdown-report.md, and references/final-report.md for the Phase-4 narrative structure). Subagents load the skills relevant to their role; skills are not user-invocable directly — use the commands above.

Bundled library — shared_utils, a Python package with a fit-and-summarize pipeline, convergence diagnostics, LOO, and ArviZ helpers. The setup command copies it into the working project as a path dependency.

How the workflow runs

1. Data understanding (eda/) — eda-analyst explores the data and surfaces competing structural hypotheses about the data-generating process.
2. Model design (design/) — analysis-planner frames the analysis (purpose, validation, domain, structural questions) and the shared baseline; parallel model-designer instances then turn the structural questions into an experiment plan.
3. Model development (experiments/) — each experiment flows through prior-predictive-checker → fake-data-checker → model-fitter → posterior-predictive-checker → critique, with model-refiner and model-selector driving iteration until questions are resolved.
4. Reporting (final_report.html) — report-writer produces a report organized around what was learned about the data-generating process.

Optional settings

Add to your .claude/settings.json:

{
  "env": {
    "CLAUDE_BASH_MAINTAIN_PROJECT_WORKING_DIR": "1"
  }
}