Seaborn (Claude Skill)
Statistical visualization with pandas integration.
| Type | Claude Skill |
| Supplier | K-Dense Inc. (community OSS) |
| Availability | GA — part of the actively maintained K-Dense scientific-agent-skills collection |
| Pricing | Free / OSS (BSD-3-Clause) |
| Capabilities | Read/Write — Claude runs the skill’s Python locally (Bash), not as an MCP tool |
How to install
- Also packaged in the SciAgent-Skills collection (jaechang-hits (community OSS, CC BY 4.0)): clone
jaechang-hits/SciAgent-Skillsand run/plugin install sciagent-skillsin Claude Code (or copyskills/data-visualization/seaborn-statistical-plotsinto~/.claude/skills/). - Claude Code / Claude.ai — Skills CLI (recommended):
npx skills add K-Dense-AI/scientific-agent-skillsInstalls the K-Dense collection; enable the
seabornskill when prompted. Works across Claude Code, Cursor, and Codex via the Agent Skills spec (requires Node ≥ 18). - Claude Code / Claude Desktop — manual clone:
git clone https://github.com/K-Dense-AI/scientific-agent-skills cp -r scientific-agent-skills/skills/seaborn ~/.claude/skills/Project-scoped alternative: copy into
.claude/skills/instead of~/.claude/skills/. The skill declares its own Python dependencies in itsSKILL.md; install them (the K-Dense skills generally useuv/pip) when prompted on first use.
What it does
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.
Primary use cases: quick exploration of distributions, relationships, and categorical comparisons with attractive defaults.
Notes
Distributed as a SKILL.md (plus code examples) in the K-Dense collection — Claude executes it locally via Bash/Python rather than as an MCP server. Upstream license: BSD-3-Clause. The skill name to enable after install is seaborn.
Sources
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