pymc-bayesian-linear-regression-starter — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited pymc-bayesian-linear-regression-starter (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill to fit a tiny Bayesian linear regression with PyMC and summarize posterior means plus credible intervals.
(x, y) table.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.jsonpython3 -m unittest discover -s skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/tests -p 'test_*.py'python3 -m unittest tests.smoke.test_frontier_domain_skills -v~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.