MCMC Diagnostics

SkillDev tools

MCMC convergence diagnostics and analysis

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the MCMC Diagnostics skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/mathematics/skills/mcmc-diagnostics/SKILL.md and read by ahel’s review.

Purpose

Provides MCMC convergence diagnostics and analysis capabilities for validating Bayesian inference results.

Capabilities

Rhat (potential scale reduction) computation
Effective sample size (ESS) calculation
Trace plot generation
Autocorrelation analysis
Divergence detection
Energy diagnostic (E-BFMI)

Usage Guidelines

Convergence Check: Verify Rhat < 1.01 for all parameters
Sample Quality: Ensure ESS is sufficient for inference
Visual Inspection: Review trace plots for mixing
Divergences: Address divergent transitions

Tools/Libraries

ArviZ
CODA
MCMCpack

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: mcmc-diagnostics
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter