MCMC Diagnostics
SkillDev toolsMCMC convergence diagnostics and analysis
Use MCMC Diagnostics in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add MCMC Diagnostics and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the MCMC Diagnostics skill
Details
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.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
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
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
mcmc-diagnostics- Source
- github.com/a5c-ai/babysitter