scientific-visualization
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
Creates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.
- Package
- 18 files, 199 KB
- SKILL.md
- 1878 words, 12+ sections
- Code
- Python (8)
- Allowed tools
ReadWriteEditBashGlobGrep- License
- MIT
- Compatibility
- Requires Python 3.11+ and uv for pinned examples. Bundled CLIs are network-free and load Matplotlib, Pillow, or pypdf only when needed. Plotly static export with Kaleido v1 requires a compatible Chrome/Chromium installation.
SKILL.md covers: Scientific Visualization · Non-negotiable guardrails · Workflow · 1. Define the evidence and destination · 2. Choose an honest encoding · 3. Design accessibility in, not after · 4. Implement with scoped styles · 5. Export explicitly and record provenance · 6. Inspect, compare, and review · Pinned snapshot · Bundled CLIs · Inspect raster/vector metadata
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 15 KB |
assets/color_palettes.py | script | 6.6 KB |
assets/nature.mplstyle | other | 1.6 KB |
assets/presentation.mplstyle | other | 1.4 KB |
assets/publication.mplstyle | other | 1.8 KB |
assets/publisher_profiles.json | config | 11 KB |
references/color_palettes.md | doc | 8.9 KB |
references/journal_requirements.md | doc | 11 KB |
references/matplotlib_examples.md | doc | 12 KB |
references/publication_guidelines.md | doc | 13 KB |
references/sources.md | doc | 10 KB |
scripts/_common.py | script | 4.5 KB |
scripts/export_plan.py | script | 19 KB |
scripts/figure_export.py | script | 23 KB |
scripts/image_metadata.py | script | 26 KB |
scripts/palette_audit.py | script | 11 KB |
scripts/style_presets.py | script | 16 KB |
scripts/style_preview.py | script | 7.6 KB |
External hosts referenced
sronpersonalpages.nl matplotlib.org pypi.org seaborn.pydata.org www.elsevier.com www.nature.com www.science.org github.com plotly.com research-figure-guide.nature.com arxiv.org journals.ieeeauthorcenter.ieee.org journals.plos.org link.springer.com pillow.readthedocs.io pubsapp.acs.org www.cell.com colorbrewer2.org doi.org export.arxiv.org pypdf.readthedocs.io
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-scientific-visualization-scan.json
- Badge for your README:
- Request a re-scan