arboreto
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
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
- Package
- 5 files, 30 KB
- SKILL.md
- 1277 words, 11 sections
- Code
- Python (1)
- License
- BSD-3-Clause license
- Compatibility
- Requires the isolated Python 3.11 compatibility stack below, including Arboreto, Dask/distributed, NumPy, pandas, scikit-learn and SciPy. Network access is needed for installation, not local inference. No credentials required.
SKILL.md covers: Arboreto · When to use · Installation and compatibility · Workflow · Run the bundled wrapper · Minimal Python example · Interpret and validate output · pySCENIC boundary · Troubleshooting · Sources and review scope · Citing Scientific Agent Skills
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 10 KB |
references/algorithms.md | doc | 4.3 KB |
references/basic_inference.md | doc | 5.1 KB |
references/distributed_computing.md | doc | 4.9 KB |
scripts/basic_grn_inference.py | script | 4.9 KB |
External hosts referenced
github.com arboreto.readthedocs.io arxiv.org docs.scipy.org docs.dask.org doi.org export.arxiv.org pypi.org scikit-learn.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-arboreto-scan.json
- Badge for your README:
- Request a re-scan