bulk-rnaseq
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
Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.
- Package
- 8 files, 71 KB
- SKILL.md
- 2040 words, 12+ sections
- Code
- Python (3)
- License
- MIT
- Compatibility
- Requires Python 3.11+ with pandas and numpy; Salmon import also needs pytximport. Read processing needs Nextflow with containers or standalone bioinformatics tools. Network access is needed for installation and reference downloads.
SKILL.md covers: Bulk RNA-seq · Overview · When to Use This Skill · The Pipeline at a Glance · Two Upstream Paths — Pick One · Setup · This skill's glue (bridge + handoffs) — Python · Downstream skills install their own deps: · pydeseq2 skill -> uv pip install pydeseq2 · pathway-enrichment skill -> uv pip install gseapy gprofiler-official · Path A (nf-core): only Nextflow + a container engine are needed — see the `nextflow` skill. · Path B (standalone tools): install via bioconda. Pin versions for reproducibility.
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 16 KB |
references/counts-and-handoff.md | doc | 8.3 KB |
references/design-and-qc.md | doc | 6.2 KB |
references/upstream-manual.md | doc | 7.0 KB |
references/upstream-nfcore.md | doc | 7.3 KB |
scripts/_tabular.py | script | 975 B |
scripts/build_counts_matrix.py | script | 14 KB |
scripts/validate_samplesheet.py | script | 10 KB |
External hosts referenced
github.com nf-co.re pytximport.complextissue.com arxiv.org bioconductor.org pydeseq2.readthedocs.io combine-lab.github.io docs.seqera.io doi.org export.arxiv.org multiqc.info subread.sourceforge.net www.bioinformatics.babraham.ac.uk
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-bulk-rnaseq-scan.json
- Badge for your README:
- Request a re-scan