bayesian-workflow

bayesian-workflow

An opinionated Agent Skill for building, diagnosing, and reporting on Bayesian statistical models using PyMC and ArviZ.

Compatible with Claude Code, Kimi Code, Cursor, Gemini CLI, and any agent that supports the Agent Skills spec.

Full breakdown here.

What it does

Guides your coding agent through the full Bayesian workflow:

1. Formulate the generative story
2. Specify priors with documented justifications
3. Implement in PyMC using modern best practices (coords, dims, nutpie)
4. Prior predictive checks before fitting
5. Inference via MCMC (nutpie sampler by default)
6. Convergence diagnostics (R-hat, ESS, trace plots, energy plots)
7. Model criticism (posterior predictive checks, LOO-PIT calibration)
8. Prior/likelihood sensitivity (power-scaling via PSIS)
9. Model comparison (LOO-CV, ELPD, stacking weights)
10. Reporting with companion analysis notes and audience-adapted reports

The skill enforces guardrails that agents won't apply on their own: 94% HDI, mandatory calibration checks, prior/likelihood sensitivity checks, non-centered parameterizations for hierarchical models, reproducible descriptive seeds, immediate save-to-disk after sampling, and xarray-first data manipulation.

Install

Claude Code

Clone and copy the skill into your personal skills directory:

git clone https://github.com/Learning-Bayesian-Statistics/baygent-skills.git /tmp/baygent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/baygent-skills/bayesian-workflow ~/.claude/skills/

For project-level installation (available only in that project), copy into .claude/skills/ at the project root instead.

Other compatible agents (Kimi Code, Cursor, etc.)

Clone the repo and copy the skill folder into your agent's skills directory:

git clone https://github.com/Learning-Bayesian-Statistics/baygent-skills.git /tmp/baygent-skills
cp -r /tmp/baygent-skills/bayesian-workflow/ ~/.config/agents/skills/bayesian-workflow/

PyMC installation

The skill recommends conda-forge / mamba-forge for PyMC and its dependencies:

mamba install -c conda-forge pymc nutpie arviz arviz-stats preliz

Example prompts

Once installed, just ask your agent naturally:

• "I have customer churn data with binary outcome plus age, tenure, and monthly spend. Build me a Bayesian logistic regression with uncertainty estimates."
• "My model has 47 divergences and R-hat of 1.03. What do I do?"
• "I have test scores for 200 students across 15 schools. Some schools only have 5 students. Help me build a hierarchical model."
• "Compare these two models and tell me which one to use. I have the InferenceData objects."
• "I need to present Bayesian results to my boss who has no stats background."

What's included

bayesian-workflow/
├── SKILL.md                          # Main workflow instructions
├── main.py                           # Entrypoint for programmatic use
├── pyproject.toml                    # Package metadata
├── references/
│   ├── priors.md                     # Prior selection guide
│   ├── diagnostics.md                # Convergence diagnostics
│   ├── model-criticism.md            # PPC, calibration, LOO-PIT
│   ├── model-comparison.md           # LOO-CV, ELPD, stacking weights
│   ├── hierarchical.md               # Partial pooling, non-centered parameterization
│   ├── sensitivity.md                # Prior/likelihood sensitivity analysis (power-scaling)
│   └── reporting.md                  # Report templates, audience adaptation
└── scripts/
    ├── diagnose_model.py             # Post-sampling diagnostics report
    └── calibration_check.py          # Calibration plots from InferenceData

License

MIT - see LICENSE.