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Pymc bayesian linear regression starter

Skill ma-compbio-lab/SkillFoundry/skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter

A framework for discovering, compiling, and validating reusable skills for scientific agents.

Install
npx -y skills add ma-compbio-lab/SkillFoundry --skill pymc-bayesian-linear-regression-starter

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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PyMC Bayesian Linear Regression Starter

Use this skill to fit a tiny Bayesian linear regression with PyMC and summarize posterior means plus credible intervals.

What it does

  • Loads a deterministic toy (x, y) table.
  • Fits a simple Bayesian regression with PyMC.
  • Summarizes posterior means, 90% intervals, and a few posterior predictive means.
  • Returns compact JSON suitable for downstream scientific inference workflows.

When to use it

  • You need a local starter for Bayesian modeling in science workflows.
  • You want a minimal example of PyMC sampling and ArviZ-based posterior summarization.

Example

slurm/envs/statistics/bin/python skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/scripts/run_pymc_linear_regression.py \
  --input skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/examples/toy_observations.tsv \
  --out scratch/pymc/linear_regression_summary.json

Verification

  • Skill-local tests: python3 -m unittest discover -s skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/tests -p 'test_*.py'
  • Repository smoke: python3 -m unittest tests.smoke.test_frontier_domain_skills -v

Keep looking

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