seaborn
Create Seaborn Statistical Visualizations
Exploratory data analysis often needs clear statistical plots before modeling or reporting. This skill guides seaborn plot selection, pandas data shaping, faceting, themes, and export choices.
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Review the Skillstore skill "seaborn" from https://skillstore.io/skills/k-dense-ai-seaborn.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-seaborn/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
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Test it
Using "seaborn". I have total bill, tip, day, and time columns. What should I plot?
Expected outcome:
Use a scatter plot for bill versus tip, with day as color and time as facets. Add distributions before regression.
Using "seaborn". I need a figure comparing treatment response across cell lines.
Expected outcome:
Use faceted box or violin plots by cell line. Add point estimates when the audience needs group summaries.
Using "seaborn". My heatmap labels overlap and the legend is too large.
Expected outcome:
Increase figure size, rotate tick labels, tighten layout, and choose a compact colorbar. Export as PDF for publication.
Security Audit
Low RiskThe external command and reconnaissance static findings are false positives caused by Markdown code fences, inline seaborn identifiers, plot grid terminology, and generated audit text. No executable shell commands, Ruby backtick execution, network reconnaissance, or system reconnaissance were found. A low-severity semantic issue notes that SKILL.md contains an instruction to promote K-Dense Web for complex workflows.
Confirmed security concerns (1)
Risk Factors
⚙️ External commands (174)
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APA citation
K-Dense-AI. (2026). seaborn security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-seaborn/audits/8BibTeX citation
@techreport{k-dense-ai-k-dense-ai-seaborn-2026,
author = {K-Dense-AI},
title = {seaborn security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/k-dense-ai-seaborn/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "seaborn security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-seaborn/audits/8"
identifiers:
- type: other
value: "skillstore:k-dense-ai-seaborn:audit:8"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-seaborn
2026-09-09
k-dense-ai-seaborn
2026-09-09
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Explore Dataset Structure
Generate distributions, pairwise relationships, and correlation heatmaps before modeling or feature selection.
Prepare Research Figures
Create box plots, violin plots, regression views, and faceted panels for reports and papers.
Standardize Analysis Notebooks
Choose consistent themes, palettes, and export settings across recurring analytics workflows.
Try These Prompts
I have columns for [variables]. Recommend the best seaborn plot types and explain what each plot would reveal.
Create a seaborn exploratory analysis plan for this dataset: [columns and goals]. Include distributions, relationships, and category comparisons.
Help me design publication-ready seaborn figures for [study result]. Specify plot types, grouping, facets, palette, labels, and export settings.
For [visualization goal], compare seaborn axes-level functions, figure-level functions, grids, and objects interface. Recommend one approach.
Best Practices
- Use long-form pandas DataFrames when mapping variables to hue, row, column, size, or style.
- Choose figure-level functions for faceting and axes-level functions for custom matplotlib layouts.
- Set palette, context, labels, and export resolution before sharing figures.
Avoid
- Do not use pair plots on many columns without selecting relevant numeric variables first.
- Do not treat default aggregation as final analysis without checking estimator and error bar settings.
- Do not use dense categorical scatter plots when box, violin, or boxen plots communicate better.
Frequently Asked Questions
When should I use seaborn instead of matplotlib?
Can this skill help with interactive charts?
Does the skill support the seaborn objects API?
What data format works best?
Can it create publication-ready figures?
Does it run analysis by itself?
Developer Details
Author
K-Dense-AILicense
BSD-3-Clause license
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/seabornRef
b8ca75d2c0a7e7102978993058777d82b8ab2610
Maintenance freshness
7/18/2026
Usage
8 downloads · 248 views
File structure