exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.
77
96%
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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
The required runtime workflow reads only attacker-supplied *local files* (e.g., `data.csv` via `scripts/eda_analyzer.py` → `scripts/_tabular.py::profile_table` → `scan_table`, which parses headers/cell text and JSON/NumPy/HDF5 metadata), so the “outsider-authored free text” is coming from the file content that is explicitly treated as untrusted data.
- Repository
- K-Dense-AI/scientific-agent-skills
- Commit
b2a92ba
- Audited
- Security analysis
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