onekgpd
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
Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome…
- Package
- 6 files, 1.1 MB
- SKILL.md
- 2679 words, 12+ sections
- Code
- Python (2)
- Allowed tools
WriteBash- License
- MIT
- Compatibility
- Requires Python >=3.11. Variant and sample queries require outbound network access to the public 1000 Genomes query endpoint over TLS; the sample/population metadata commands run fully offline over a data file bundled in the skill. No credentials, API keys, or environment variables are used.
SKILL.md covers: OneKGPd: Individual-Level Queries over the 1000 Genomes Project · Scope · When to Use · Prerequisites · Core Rules · Coordinate Provenance (MANDATORY FIRST STEP) · Resolve gene symbol -> GRCh38 region with an authoritative source FIRST, · then pass the verified coordinates to the OneKGPd query below. · Command Selection Guide · Cohort interpretation · Annotation filters (shared across variant and sample selection/counting) · Quick Start
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 22 KB |
assets/kgpe.json | config | 1013 KB |
references/annotation_vocabularies.md | doc | 4.7 KB |
references/onekgpd_commands.md | doc | 18 KB |
scripts/onekgpd_api.py | script | 32 KB |
scripts/onekgpd_meta.py | script | 19 KB |
External hosts referenced
github.com www.internationalgenome.org www.ncbi.nlm.nih.gov dnaerys.org docs.astral.sh pypi.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.
Environment variables referenced
OPEN_CHROMATIN_REGION PROMOTER_FLANKING_REGION REGULATORY_REGION_ABLATION REGULATORY_REGION_AMPLIFICATION REGULATORY_REGION_VARIANT SPLICE_DONOR_REGION_VARIANT SPLICE_REGION_VARIANT
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-onekgpd-scan.json
- Badge for your README:
- Request a re-scan