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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill data-visualization命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.
Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.
Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.
正在显示 SKILL.md
| 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","version":"1.0.0"} |
This skill enables an AI agent to transform structured data into meaningful visual representations. The agent selects appropriate chart types based on the data and the question being asked, builds publication-quality static charts with matplotlib and seaborn, and creates interactive visualizations with plotly. It follows established data visualization principles to ensure clarity, accuracy, and visual appeal.
Understand the data and the question. Examine the dataset's structure — how many variables, what types (numeric, categorical, temporal), and what relationship or comparison the user wants to highlight. The question drives chart selection more than the data alone.
Select the appropriate chart type. Match the analytical goal to the right visual form. Use bar charts for categorical comparisons, line charts for trends over time, scatter plots for relationships between two continuous variables, histograms for distributions, box plots for spread and outliers, and heatmaps for correlation matrices or dense categorical grids.
Prepare the data for plotting. Aggregate, pivot, or reshape the data as needed. Sort categorical axes by value for bar charts. Resample time-series to the right granularity. Ensure no NaN values leak into the plot that would create gaps or errors.
Build the visualization with appropriate styling. Apply consistent color palettes, readable axis labels, descriptive titles, and proper legends. Remove chart junk — unnecessary gridlines, borders, and decorations. Use figure sizes that match the intended output medium (report, slide, dashboard).
Add context and annotations. Highlight key data points with annotations, reference lines, or shaded regions. Add summary statistics directly on the chart where helpful (e.g., median line on a box plot, trend line on a scatter). Context turns a chart from decoration into analysis.
Export or display. Save static charts as PNG or SVG for reports, or render interactive HTML for dashboards and exploration. Set DPI to 150+ for print-quality output.
| Goal | Chart Type | Library |
|---|---|---|
| Compare categories | Bar chart (vertical or horizontal) | matplotlib, seaborn |
| Show trend over time | Line chart | matplotlib, plotly |
| Explore relationship between 2 variables | Scatter plot | seaborn, plotly |
| Show distribution of a variable | Histogram or KDE | seaborn |
| Compare distributions across groups | Box plot or violin plot | seaborn |
| Display correlation matrix | Heatmap | seaborn |
| Show composition / proportions | Stacked bar or pie chart | matplotlib |
| Enable user exploration | Interactive chart | plotly |
Provide the agent with a dataset and a description of what you want to visualize. Optionally specify chart type, color preferences, output format, and figure dimensions. The agent will select the best approach if no chart type is specified.
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.
alpha=0.3), jitter, or hexbin/2D density plots when thousands of points overlap.