pymc SKILL.md skillA
pymc from marielynneblock/arcanum-artifex is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add marielynneblock/arcanum-artifex/pymc@git:20260710.0115f49Pins git:20260710.0115f49 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- marielynneblock/arcanum-artifex
- Path
skills/data-science/pymc/SKILL.md- Versions recorded
- 5
- Latest version
- git:20260710.0115f49, committed Jul 10, 2026
- First version recorded
- Apr 6, 2026
- Size
- 15,778 bytes
- Repository stars
- 4
- License
- none declared
Bayesian modelling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# PyMC Bayesian Modelling ## Overview PyMC is a Python library for Bayesian modelling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC). ## When to Use This Skill This skill should be used when: - Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.) - Performing MCMC sampling or variational inference - Conducting prior/posterior predictive checks - Diagnosing sampling issues (divergences, convergence, ESS) - Comparing multiple models using information criteria (LOO, WAIC) - Implementing uncertainty quantification through Bayesian methods - Working with hierarchical/multilevel data structures - Handling missing data or measurement error in a principled way ## Standard Bayesian Workflow Follow this workflow for building and validating Bayesian models: ### 1. Data Preparation ```python import pymc as pm import arviz as az import numpy as np # Load and prepare data X = ... # Predictors y = ... # Outcomes # Standardize predictors for better sampling …
Read the whole file at its exact version.
How to install
mdr add marielynneblock/arcanum-artifex/pymc@sha256:a1bfb3c9c266dde6The line at the top of this page pins the label git:20260710.0115f49, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_xt7traxoqfcdhoex)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Apr 6, 2026 and Jul 10, 2026, about one every 24 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260710.0115f49 latest | 2026-07-10 | 0115f49 | 15,778 B | A | view · diff |
| git:20260710.d1b3aa8 | 2026-07-10 | d1b3aa8 | 15,779 B | A | view · diff |
| git:20260710.ffb3400 | 2026-07-10 | ffb3400 | 15,779 B | A | view · diff |
| git:20260515.b861afb | 2026-05-15 | b861afb | 15,779 B | A | view · diff |
| git:20260406.05dff34 | 2026-04-06 | 05dff34 | 15,780 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (15778 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
marielynneblock/arcanum-artifex · 4 stars · license none declared · pushed 2026-09-19 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_xt7traxoqfcdhoex GET https://markdownregistry.com/api/v1/resolve?ref=marielynneblock/arcanum-artifex/pymc GET https://markdownregistry.com/api/v1/blob/a1bfb3c9c266dde6ddbfb7194f2741919ed4b00d00abecffa424030a23887ada
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
More from marielynneblock/arcanum-artifex
Every file in marielynneblock/arcanum-artifex