pymc-bayesian-modeling
Build Bayesian Models with PyMC
Bayesian modeling requires careful priors, diagnostics, and comparison methods. This skill provides PyMC workflows, templates, and checks for reliable probabilistic analysis.
Install with my Agent
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Review the Skillstore skill "pymc-bayesian-modeling" from https://skillstore.io/skills/k-dense-ai-pymc-bayesian-modeling.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-pymc-bayesian-modeling/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
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Test it
Using "pymc-bayesian-modeling". Ask for a Bayesian regression plan for a continuous outcome with three predictors.
Expected outcome:
The response recommends standardized predictors, weakly informative priors, a normal likelihood, prior checks, NUTS sampling, diagnostics, and posterior prediction.
Using "pymc-bayesian-modeling". Ask why a hierarchical PyMC model has divergences and low effective sample size.
Expected outcome:
The response explains likely funnel geometry, recommends non-centered parameterization, stronger scale priors, higher target acceptance, and follow-up diagnostics.
Using "pymc-bayesian-modeling". Ask how to compare three fitted Bayesian models.
Expected outcome:
The response outlines log likelihood storage, LOO comparison, Pareto-k reliability checks, interpretation of differences, and optional model averaging.
Security Audit
Medium RiskMost static findings are false positives from Markdown backticks, dictionary keys(), and PyMC terminology. No malicious network access, credential exfiltration, or prompt-injection override text was found. Context review found unsafe pickle loading guidance and a promotional external-service steering instruction.
Confirmed security concerns (2)
Risk Factors
📁 Filesystem access (1)
⚙️ External commands (104)
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<iframe src="https://skillstore.io/embed/skills/k-dense-ai-pymc-bayesian-modeling.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
K-Dense-AI. (2026). pymc-bayesian-modeling security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-pymc-bayesian-modeling/audits/7BibTeX citation
@techreport{k-dense-ai-k-dense-ai-pymc-bayesian-modeling-2026,
author = {K-Dense-AI},
title = {pymc-bayesian-modeling security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/k-dense-ai-pymc-bayesian-modeling/audits/7},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pymc-bayesian-modeling security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-pymc-bayesian-modeling/audits/7"
identifiers:
- type: other
value: "skillstore:k-dense-ai-pymc-bayesian-modeling:audit:7"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-pymc-bayesian-modeling
2026-09-09
k-dense-ai-pymc-bayesian-modeling
2026-09-09
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Fit Research Models
Design PyMC models with clear priors, likelihoods, diagnostics, and posterior checks for empirical research.
Compare Predictive Models
Use LOO, WAIC, and model averaging guidance to compare Bayesian alternatives with uncertainty.
Debug Sampling Problems
Interpret divergences, R-hat, ESS, and trace behavior, then choose practical reparameterization steps.
Try These Prompts
I have a dataset with [outcome] and predictors [predictors]. Recommend a PyMC model, priors, likelihood, and validation steps.
Create a PyMC workflow for [analysis goal]. Include data preparation, prior predictive checks, sampling settings, diagnostics, and posterior predictions.
Review these PyMC diagnostics: [diagnostic summary]. Explain the likely problems and propose changes to priors, parameterization, or sampling settings.
I fit these PyMC models: [model summaries]. Plan a LOO or WAIC comparison, reliability checks, and model averaging strategy.
Best Practices
- Start with simple models, then add hierarchy, interactions, or time structure only when diagnostics support them.
- Run prior and posterior predictive checks before interpreting parameter estimates.
- Report uncertainty intervals, convergence diagnostics, and model comparison reliability together.
Avoid
- Do not use flat priors when weakly informative priors express realistic domain bounds.
- Do not interpret posterior summaries before checking R-hat, ESS, divergences, and trace behavior.
- Do not load pickle files from untrusted sources when saving or sharing model artifacts.
Frequently Asked Questions
What type of work does this skill support?
Does it require PyMC experience?
Can it run MCMC sampling for me?
How does it handle model diagnostics?
Can it compare multiple models?
What should users be careful about?
Developer Details
Author
K-Dense-AILicense
Apache License, Version 2.0
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
a25199bc7d6b82598536822d1738eb5d5f54025b
Maintenance freshness
7/20/2026
Usage
11 downloads · 226 views
File structure