relsa-severity-assessment
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a…
- Package
- 9 files, 123 KB
- SKILL.md
- 2678 words, 12+ sections
- Code
- Python (4)
- Allowed tools
ReadWriteEditBash- License
- MIT
- Compatibility
- Requires Python >=3.12 with numpy, pandas, scipy, statsmodels and matplotlib. Local analysis needs no network or credentials; installation and upstream review need network access.
SKILL.md covers: RELSA severity assessment and exploratory score forecasting · Overview · When to use this skill · Installation · Data format · The four decisions that determine the result · Workflow · Step 1 — compute RELSA scores · Step 2 — forecast a score at a known evaluation time · Step 3 — put the score in context with severity zones · Boundaries: state these when you report · Reporting checklist
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 20 KB |
assets/example_cohort.csv | data | 2.1 KB |
references/forecasting.md | doc | 6.6 KB |
references/relsa-method.md | doc | 7.4 KB |
references/thresholds-and-zones.md | doc | 4.8 KB |
scripts/_common.py | script | 14 KB |
scripts/forecast_relsa.py | script | 31 KB |
scripts/kde_thresholds.py | script | 15 KB |
scripts/relsa_score.py | script | 22 KB |
External hosts referenced
doi.org github.com arxiv.org environment.ec.europa.eu eur-lex.europa.eu export.arxiv.org pkg.robjhyndman.com stat.ethz.ch www.statsmodels.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-relsa-severity-assessment-scan.json
- Badge for your README:
- Request a re-scan