scientific-visualization
Create and audit truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.
72
89%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
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 only outsider-authored free text the runtime reads is from the user-supplied local input figure files (e.g., SVG/PDF/EPS/text inside the provided file) via `scripts/image_metadata.py:inspect_file()` → `inspect_svg()`/`inspect_pdf()`/`inspect_eps()`; there is no queue/feed ingestion or automatic search/fetch of external text.
- Repository
- K-Dense-AI/scientific-agent-skills
- Commit
9e8b0cb
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.