Skill profile

seaborn

Statistical visualization with pandas integration.

What this skill does for you

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

Quoted from the skill description in the pinned source

Who made it

Publisherk-dense-ai

Repositoryscientific-agent-skills

Installs1,800as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the seaborn skill from k-dense-ai/scientific-agent-skills.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

Scanner	Date	Result
Edge static checks	2026-09-27	Pass
Cisco skill-scanner	2026-09-27	Pass
Semgrep Edge rules	2026-09-27	Pass

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Socket (skills.sh mirror): Version unavailable. Scope: Not supplied by the mirrored record. Revision: not supplied. Raw findings: not supplied. Counted findings: Not supplied.

Snyk (skills.sh mirror): Version unavailable. Scope: Not supplied by the mirrored record. Revision: not supplied. Raw findings: not supplied. Counted findings: Not supplied.

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: 49c6e97775eaa18ba791bebe23162a70ae601c18. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: 49c6e97775eaa18ba791bebe23162a70ae601c18. Raw findings: . Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: 49c6e97775eaa18ba791bebe23162a70ae601c18. Raw findings: . Counted findings: .

Source and licence

Repository: https://github.com/k-dense-ai/scientific-agent-skills
Pinned skill file: View source at revision
Revision: 49c6e97775eaa18ba791bebe23162a70ae601c18
Package hash: sha256:7664f445568f69dfed1648a395cd39bfc2882c52a38c3a41c0403891033e585a
Licence: MIT