pydoe3-experimental-design-starter — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited pydoe3-experimental-design-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 generate a tiny full-factorial design from bounded factor definitions and summarize the resulting experiment table.
2^k full-factorial design with pyDOE3./slurm/envs/statistics/bin/python skills/statistical-and-machine-learning-foundations-for-science/pydoe3-experimental-design-starter/scripts/run_pydoe3_experimental_design.py --input skills/statistical-and-machine-learning-foundations-for-science/pydoe3-experimental-design-starter/examples/toy_factors.json --out scratch/statistics/experimental_design_summary.json~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.