Plan Bayesian model structures and MCMC diagnostics for PyMC workflows.
npx skills add https://github.com/MarvinCui/NeuroForge --skill pymc-marvincui
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/MarvinCui/NeuroForge/tree/main/NeuroForge/skills/pymc Command: npx skills add https://github.com/MarvinCui/NeuroForge --skill pymc-marvincui
This Skill helps you plan Bayesian model structure and inference workflows using PyMC so you can reason safely about priors, likelihoods, sampling, and diagnostics before running heavy computations.
Ask the AI: "Given my dataset and the hypothesis I want to test, propose a PyMC Bayesian model plan with recommended priors, likelihood, hierarchical structure (if needed), and which diagnostics to check before interpreting results."
High-intent search queries and answers about installing and using this skill.
To plan a Bayesian model structure in PyMC, you define model components for behavioral or imaging data by specifying appropriate likelihood choices and hierarchical structures before running any heavy computations.
For MCMC convergence diagnostics in PyMC, you should inspect chain and acceptance behavior, along with trace and summary expectations, to ensure sampling reliability before interpreting the posterior results.
Choosing priors and planning posterior predictive checks involves outlining prior specifications and posterior summarization strategies as guidance, ensuring safe inference planning before executing actual sampling.
No, this PyMC guidance does not run heavy sampling automatically; it avoids executing long or computationally heavy sampling without explicit user confirmation to ensure safe inference workflows.
Yes, you can use PyMC Bayesian planning in conjunction with related skills for EEG or imaging summaries, keeping the focus specifically on Bayesian model planning and routing within the broader workflow.
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