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model-diagnostics

Identify and summarize MCMC convergence diagnostics for Bayesian models.

9|1|Updated Jan 10, 2026
Search Tags:#diagnostics#mcmc#bayesian#stan#jags#rhat
One-click install
npx skills add https://github.com/choxos/BiostatAgent --skill model-diagnostics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-diagnostics
Source: https://github.com/choxos/BiostatAgent/tree/main/plugins/bayesian-modeling/skills/model-diagnostics
Command: npx skills add https://github.com/choxos/BiostatAgent --skill model-diagnostics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides structured guidance for assessing and diagnosing convergence and fit in Bayesian models, enabling reliable interpretation of MCMC results.

Core Features & Use Cases

  • Diagnoses convergence issues using standard metrics (Rhat, ESS, divergences) and identifies potential problems in Stan, JAGS, or PyMC fits.
  • Provides practical workflows for model checking, posterior predictive checks, and efficient troubleshooting across common Bayesian pipelines.
  • Includes examples and best practices for reporting diagnostics in research and production settings.

Quick Start

Run diagnostics on an active Bayesian model fit to generate convergence metrics and PPC plots.

Frequently Asked Questions about model-diagnostics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check MCMC convergence diagnostics for Stan or PyMC models?▼

MCMC convergence diagnostics extract Rhat, ESS, and divergence metrics from Stan, PyMC, or JAGS fits to ensure proper posterior interpretation. The skill summarizes these convergence metrics to identify potential sampling problems.

What do high Rhat or low ESS values indicate in Bayesian diagnostics?▼

High Rhat and low ESS values in Bayesian diagnostics indicate that MCMC chains have not properly converged or explored the parameter space efficiently. The skill identifies these convergence issues and guides troubleshooting steps.

How do I run posterior predictive checks after fitting a Bayesian model?▼

Posterior predictive checks validate model fit by comparing simulated data to observed data. The skill generates PPC plots and provides practical workflows for model checking across common Bayesian pipelines.

Does this skill work with JAGS and WinBUGS or only Stan models?▼

The skill works with Stan, PyMC, JAGS, and WinBUGS runs. It applies standard convergence metrics, divergences, and treedepth diagnostics across these common Bayesian frameworks to guide troubleshooting.

How do I troubleshoot divergences and treedepth issues during MCMC sampling?▼

Troubleshooting divergences and treedepth issues involves extracting these diagnostic metrics from your Bayesian fit and following structured guidance to adjust sampler parameters. The skill provides practical workflows for resolving these convergence failures.

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