Updated 1w ago
by pymc-labs
pymc-modeling
LLM agent skill files for PyMC and PyTensor
90 GitHub stars (90)
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- 4 skills (4 skills found in its SKILL.md files)
- 10 forks (10 forks)
- 1w ago last update (Last updated 1w ago)
Install
Skills CLI
npx skills add pymc-labs/pymc-modeling
Asks which skills and agents to install. Just one: npx skills add pymc-labs/pymc-modeling --skill arviz-diagnostics — add -g for every project.
Claude Code
git clone https://github.com/pymc-labs/pymc-modelingcp -r "pymc-modeling/skills/arviz-diagnostics" ~/.claude/skills/
Copies the arviz-diagnostics skill into ~/.claude/skills/ for every project (use .claude/skills/ for one project). Repeat for the other skills listed below.
Plugin
/plugin marketplace add pymc-labs/pymc-modeling
Run inside Claude Code if the repository ships a plugin marketplace, then pick plugins from /plugin.
Skills can run code on your machine — read the SKILL.md first.
What it does
In the author's own words, from each skill's SKILL.md.
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arviz-diagnostics
Diagnose existing Bayesian inference output and evaluate fitted predictions with modern ArviZ DataTree summaries, divergences, rank R-hat, ESS and Monte Carlo precision. Use for posterior predictive checks, calibration, LOO/ELPD, Pareto k, stacking, grouped or temporal validation, survival diagnostics and Bayes-factor interpretation.
skills/arviz-diagnostics/SKILL.md on GitHub (opens in a new tab) -
prior-elicitation
Elicit and check Bayesian prior assumptions in meaningful units. Use for expert quantiles, PreliZ, constrained priors, prior-predictive plausibility and prior sensitivity, including power scaling, regularized horseshoe and R2D2 assumptions.
skills/prior-elicitation/SKILL.md on GitHub (opens in a new tab) -
pymc-modeling
Build, revise, and debug Bayesian models with PyMC. Use when choosing likelihoods and priors, specifying model/data dimensions, fitting hierarchical models, GPs, time series, mixtures, BART or splines, selecting inference methods, resolving failed initial evaluation or model/data shape errors, generating predictions, or testing model code with simulated data and simulation-based calibration (SBC)…
skills/pymc-modeling/SKILL.md on GitHub (opens in a new tab) -
pytensor-workflows
Build, differentiate, compile, and debug PyTensor symbolic computations for models. Use for symbolic tensor shape or dtype errors, named dimensions, indexing and broadcasting, vectorization and scan, numerical or sparse operations, symbolic RNG state, graph transformations and profiling, or custom differentiable Ops and backend lowering.
skills/pytensor-workflows/SKILL.md on GitHub (opens in a new tab)
No skill matches that filter.
Review before you install
A skill is a set of instructions and scripts your agent will follow, and it can run code on your machine. Read the SKILL.md and any scripts first — this listing is not a security review.
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