experimental-design
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
SkillSpector found no issues but could not fully inspect the package, usually because SKILL.md refers to a file that is not present, so it fails closed to CAUTION instead of SAFE. Read it as "not fully checked", not as a finding. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Designs experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs.…
- Package
- 7 files, 65 KB
- SKILL.md
- 1940 words, 12+ sections
- Code
- Python (2)
- Allowed tools
ReadWriteEditBash- License
- MIT license
- Compatibility
- Requires Python >=3.12 with numpy, pandas, and pydoe 1.5.0 (DOE matrices). Network access is needed only to install packages; no credentials are required.
SKILL.md covers: Experimental Design · Overview · When to Use This Skill · Installation · Choosing a design · Generating the design · Randomization / allocation schedules — `scripts/randomization.py` · Permuted blocks balance at completed-block boundaries. · A partial final block or interim prefix need not have the requested ratio. · Balance a prognostic variable across arms by randomizing within each stratum · Randomize whole clusters, not individuals (the cluster is the unit) · DOE matrices — `scripts/doe_designs.py`
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 16 KB |
references/design_types.md | doc | 7.8 KB |
references/factorial_and_doe.md | doc | 8.9 KB |
references/randomization_and_blocking.md | doc | 7.6 KB |
references/sequential_and_adaptive.md | doc | 6.4 KB |
scripts/doe_designs.py | script | 9.4 KB |
scripts/randomization.py | script | 9.4 KB |
External hosts referenced
arxiv.org pydoe.github.io www.fda.gov www.itl.nist.gov docs.rpact.org doi.org eda.nc3rs.org.uk export.arxiv.org github.com keaven.github.io www.consort-spirit.org
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-experimental-design-scan.json
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