Guide · Agent skill
How to use the Seaborn Statistical Plots agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The Seaborn Statistical Plots skill teaches an AI agent to build statistical charts with Seaborn, a Python plotting library, from pandas DataFrames. It covers distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. After this guide you will know what the skill changes, how to install it, how to ask for plots in plain language, and what to check before using it on sensitive data.
Seaborn Statistical PlotsK-Dense-AI/scientific-agent-skills · MIT Download the skill Quick start
- 1Download and unzip the skill. Download the skill ZIP from this page and unzip it so the seaborn folder contains SKILL.md.
- 2Place it in the right folder. Move the folder to ~/.claude/skills/ for personal use or to .claude/skills/ inside a project. Alternatively, upload it under Settings → Capabilities → Skills in the Claude apps.
- 3Install Seaborn in your Python environment. Run the pinned install command shown in the install section so the examples match the tested setup.
- 4Make a natural request. Describe the chart, name your DataFrame or CSV, and state the question the plot should answer.
- 5Review the generated code. Read the script the agent writes, check column names and any aggregation or missing-data choices, then run it.
What the skill makes the agent do
Without the skill, an agent may write plotting code from general memory. With it, the agent follows instructions written for Seaborn specifically: it works with named DataFrame columns, picks a plot type that fits the question, and handles aggregation, uncertainty, and missing data explicitly instead of leaving them implicit.
The skill covers both the function interface, which offers specialised functions for each plot type, and the objects interface, a chained API similar to ggplot2. For complex layered figures or programmatic plot generation the objects interface can be simpler, but the skill notes that it is still described upstream as experimental and incomplete, so the function interface is the conservative default.
- Distribution plots, relationship plots, categorical comparisons, regression displays, pair plots, and heatmaps
- Long-form (tidy) and wide-form data handling, with a conversion pattern from wide to long
- Guidance to recommend plotly for interactive figures and a separate scientific-visualization skill for publication styling
Install the skill: ZIP download or Claude app upload
Download the skill ZIP from this page and unzip it. For a personal install, place the skill folder inside ~/.claude/skills/. For a single project, place it inside that project's .claude/skills/ folder. Either way the folder should contain SKILL.md at its top level, along with the references folder.
If you use the Claude apps instead, upload the skill under Settings → Capabilities → Skills. Seaborn itself still needs to be installed in the Python environment the agent uses. The skill's own install commands pin Seaborn to a specific version for reproducible examples.
- Personal install: ~/.claude/skills/seaborn/
- Project install: .claude/skills/seaborn/ inside your project
- Claude apps: Settings → Capabilities → Skills, then upload
uv pip install "seaborn==0.13.2"
uv pip install "seaborn[stats]==0.13.2"Trigger the skill with natural requests
You do not need a special command. Describe the chart you want, name your data file or DataFrame, and mention the question you are trying to answer. The skill's description covers distributions, relationships, categorical comparisons, regression, pair plots, and heatmaps, so requests that name one of these are the most likely to use it.
Be specific about the columns and the grouping you want. If you care about uncertainty or missing values, say so; the skill is written to handle those explicitly.
- Make a pair plot of the numeric columns in sales.csv, colored by region.
- Show the distribution of order_value for each customer segment as overlaid histograms.
- Plot tip against total_bill with a regression line and confidence band.
- Create a heatmap of the correlation matrix for these survey columns.
Example: a first exploratory plot
The skill recommends loading data with pandas and passing the DataFrame to Seaborn with named variables. The quick start below uses Seaborn's bundled tips example dataset, which is downloaded from the internet the first time it is loaded if it is not cached.
For your own data, replace the load line with pd.read_csv on a local file. The agent should then pass column names as keyword arguments, as the skill requires.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('data.csv')
sns.pairplot(df, hue='target_variable', corner=True, diag_kind='kde')
plt.savefig('overview.png', dpi=300, bbox_inches='tight')What it needs before you start
The skill requires Python with Seaborn, NumPy, pandas, and Matplotlib. Python 3.12 or newer is the recommended setup for the tested stack, and the minimum listed is 3.8. Optional packages add advanced regression and clustering features: scipy, statsmodels, and fastcluster, plus ipywidgets for notebook controls.
The skill's permissions list Read, Write, Edit, and Bash, and it can run scripts and use the network.
- Required: Python, Seaborn, NumPy, pandas, Matplotlib
- Optional: scipy, statsmodels, fastcluster, ipywidgets
- Recommended: Python 3.12 or newer
Safety notes: scripts, network, and private data
This skill runs scripts and uses the network. Network access is needed for installation and for loading uncached example datasets. The function sns.load_dataset() downloads public CSV files from a repository that can change over time, so the skill advises recording the source revision or file hash for reproducible work.
For private, regulated, or offline data, do not use load_dataset. Load local files explicitly with pandas and pass the DataFrame to Seaborn. Review any code the agent writes before running it on sensitive material.
- Use pd.read_csv or similar for private or regulated data
- Keep a record of the dataset source for reproducibility
- Review agent-written scripts before running them
Tips for better results
Give the agent tidy data: one variable per column and one observation per row. If your data is wide, ask the agent to reshape it first. Keep subject or sample IDs when reshaping, because rows from the same subject are not independent replicates.
Use the function interface for routine work. When a categorical plot should give each category its own colour, the skill says to assign a redundant hue and set the legend off, rather than passing palette alone. Ask for colourblind-safe palettes if your audience needs them.
- Use tidy long-form data where possible
- Keep subject IDs when reshaping, and treat repeated measures as dependent
- Name the aggregation or confidence method you want rather than relying on defaults
- For interactive charts, ask for plotly instead
What people use it for
- Scanning a CSV of survey responses with a pair plot to spot relationships between numeric columns.
- Comparing the distribution of a measurement across groups with overlaid or faceted histograms.
- Checking a regression trend with a confidence band in a teaching notebook.
- Building a correlation heatmap for a set of numeric features before modelling.
Frequently asked questions
Is the Seaborn agent skill free to use?
The skill is distributed under a licence, but the directory listing says MIT while the SKILL.md front matter says BSD-3-Clause. Check the LICENSE file in the downloaded ZIP to confirm the terms before reusing the skill. Seaborn itself is a Python library you install separately.
What do I need installed to use the Seaborn skill?
You need Python with Seaborn, NumPy, pandas, and Matplotlib. Python 3.12 or newer is recommended for the tested setup, and optional scipy, statsmodels, fastcluster, and ipywidgets add advanced features.
Where do I put the skill folder?
Put it in ~/.claude/skills/ for personal use or in a project's .claude/skills/ folder for one project. In the Claude apps you can instead upload it under Settings → Capabilities → Skills.
Can the skill make interactive dashboards?
No. The skill is for static, exploratory plots. Its description says interactive figures are better handled by the plotly skill, and the skill recommends that for interactive work.
Is it safe to use with private or regulated data?
Use local files loaded with pandas for private data. The sns.load_dataset() function downloads public example data from the internet, so avoid it for sensitive or offline work, and review any scripts the agent runs.
How does it compare with plotting directly in matplotlib?
Seaborn works directly with DataFrames and named columns, adds automatic statistical estimates and confidence intervals, and applies sensible default themes. Matplotlib gives lower-level control, and Seaborn exposes Matplotlib axes so you can customise its output further.
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Our verdict
Choose this skill if you want an agent to produce quick, well-structured statistical charts from CSV files or DataFrames in Python notebooks or scripts. Pick something else if you need interactive dashboards, which the plotly skill handles better, or if your data is sensitive and you cannot review the scripts the agent runs.
Download the skill