Data Visualization

What it does

When your agent uses it

• The user requests data visualization
• Provides relevant inputs for this workflow

Example prompts

• “/data-visualization”

Requirements

• Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Understand the data and the question. Examine the dataset's structure — how many variables, what types (numeric, categorical, temporal)…
2. 2Select the appropriate chart type. Match the analytical goal to the right visual form. Use bar charts for categorical comparisons, line…
3. 3Prepare the data for plotting. Aggregate, pivot, or reshape the data as needed. Sort categorical axes by value for bar charts. Resample…
4. 4Build the visualization with appropriate styling. Apply consistent color palettes, readable axis labels, descriptive titles, and proper…
5. 5Add context and annotations. Highlight key data points with annotations, reference lines, or shaded regions. Add summary statistics…
6. 6Export or display. Save static charts as PNG or SVG for reports, or render interactive HTML for dashboards and exploration. Set DPI to…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Visualization loads about 2k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 717 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~54

When it runs · the whole SKILL.md, loaded when a task matches

~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 717 words, ~1,971 tokens.

name

data-visualization

description

Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.

license

MIT

metadata.author

awesome-ai-agent-skills

metadata.version

1.0.0

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv("quarterly_sales.csv", parse_dates=["date"])
sns.set_theme(style="whitegrid", palette="viridis")

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle("Q4 2024 Sales Dashboard", fontsize=16, fontweight="bold")

# 1. Monthly revenue trend
monthly = df.resample("M", on="date")["revenue"].sum()
axes[0, 0].plot(monthly.index, monthly.values, marker="o", linewidth=2)
axes[0, 0].set_title("Monthly Revenue Trend")
axes[0, 0].set_ylabel("Revenue ($)")
axes[0, 0].tick_params(axis="x", rotation=45)

# 2. Revenue by region (horizontal bar)
region = df.groupby("region")["revenue"].sum().sort_values()
axes[0, 1].barh(region.index, region.values, color=sns.color_palette("viridis", len(region)))
axes[0, 1].set_title("Revenue by Region")
axes[0, 1].set_xlabel("Total Revenue ($)")

# 3. Units sold distribution (histogram)
axes[1, 0].hist(df["units_sold"], bins=30, edgecolor="white", alpha=0.8)
axes[1, 0].axvline(df["units_sold"].median(), color="red", linestyle="--", label="Median")
axes[1, 0].set_title("Units Sold Distribution")
axes[1, 0].legend()

# 4. Revenue vs. discount scatter with regression
sns.regplot(data=df, x="discount", y="revenue", ax=axes[1, 1],
            scatter_kws={"alpha": 0.4, "s": 15}, line_kws={"color": "red"})
axes[1, 1].set_title("Revenue vs. Discount")

plt.tight_layout()
plt.savefig("sales_dashboard.png", dpi=150, bbox_inches="tight")
plt.show()

import pandas as pd
import plotly.express as px

df = pd.read_csv("global_sales.csv")

# Interactive scatter with size, color, and hover data
fig = px.scatter(
    df,
    x="marketing_spend",
    y="revenue",
    size="units_sold",
    color="region",
    hover_data=["product_name", "quarter"],
    title="Marketing Spend vs Revenue by Region",
    labels={
        "marketing_spend": "Marketing Spend ($)",
        "revenue": "Revenue ($)",
        "units_sold": "Units Sold"
    },
    template="plotly_white"
)

fig.update_traces(marker=dict(opacity=0.7, line=dict(width=1, color="DarkSlateGrey")))

# Add a trend line annotation
fig.add_annotation(
    x=45000, y=320000,
    text="Strong ROI cluster:<br>low spend, high revenue",
    showarrow=True, arrowhead=2,
    font=dict(size=12, color="darkblue")
)

fig.write_html("interactive_scatter.html")
fig.show()
# Users can hover over points to see product_name and quarter,
# zoom into clusters, and toggle regions on/off via the legend.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in data-and-analytics/data-visualization of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5