seaborn – Data & Science skill for AI agents
Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.
Install the seaborn skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/seaborn-77cb3.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /seaborn – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/seaborn-77cb3.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $seaborn – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/seaborn-77cb3.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /seaborn – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/seaborn-77cb3.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /seaborn – then describe the task you want done.
SKILL.md content
Seaborn Statistical Visualization
Detailed Guide
Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use
- You need publication-quality statistical graphics directly from tabular datasets.
- You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.
- You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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