arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
In `scripts/basic_grn_inference.py`, the outsider-provided `expression_file`/`tf_file` paths are loaded with `pd.read_csv()` and `load_tf_names()` at runtime and their free-text TSV/list contents are ingested directly into `grnboost2`, making injection possible via user-authored data files.
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- K-Dense-AI/scientific-agent-skills
- Commit
b2a92ba
- Audited
- Security analysis
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