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将查询结果或数据表快速生成适合报告与演示的高质量图表。
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "create-viz" 技能： 1. 下载 https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/data/skills/create-viz/SKILL.md 2. 保存为 ~/.claude/skills/create-viz/SKILL.md 3. 装好后重载技能，告诉我可以用了
请用 Python 把这个按月销售数据做成适合汇报的折线图，突出增长趋势，添加标题、坐标轴标签和关键峰值标注，并输出可复用代码。
一段可运行的 Python 绘图代码，生成清晰美观的销售趋势折线图。
我有一个包含产品类别和销售额的 DataFrame。请判断最合适的图表类型，并生成用于比较各类别表现的高质量图表代码，配色专业、适合报告。
推荐合适图表类型，并提供用于类别对比的高质量 Python 可视化代码。
请将这份数据做成交互式图表，支持悬停查看数值、缩放和图例筛选，并确保适合嵌入网页或分享给团队查看。
生成支持交互操作的图表代码或嵌入结果，便于探索和分享数据。
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.
/create-viz <data source> [chart type] [additional instructions]
Determine:
If data warehouse is connected and data needs querying:
If data is pasted or uploaded:
If data is from a previous analysis in the conversation:
If the user didn't specify a chart type, recommend one based on the data and question:
| Data Relationship | Recommended Chart |
|---|---|
| Trend over time | Line chart |
| Comparison across categories | Bar chart (horizontal if many categories) |
| Part-to-whole composition | Stacked bar or area chart (avoid pie charts unless <6 categories) |
| Distribution of values | Histogram or box plot |
| Correlation between two variables | Scatter plot |
| Two-variable comparison over time | Dual-axis line or grouped bar |
| Geographic data | Choropleth map |
| Ranking | Horizontal bar chart |
| Flow or process | Sankey diagram |
| Matrix of relationships | Heatmap |
Explain the recommendation briefly if the user didn't specify.
Write Python code using one of these libraries based on the need:
Code requirements:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# Set professional style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")
# Create figure with appropriate size
fig, ax = plt.subplots(figsize=(10, 6))
# [chart-specific code]
# Always include:
ax.set_title('Clear, Descriptive Title', fontsize=14, fontweight='bold')
ax.set_xlabel('X-Axis Label', fontsize=11)
ax.set_ylabel('Y-Axis Label', fontsize=11)
# Format numbers appropriately
# - Percentages: '45.2%' not '0.452'
# - Currency: '$1.2M' not '1200000'
# - Large numbers: '2.3K' or '1.5M' not '2300' or '1500000'
# Remove chart junk
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('chart_name.png', dpi=150, bbox_inches='tight')
plt.show()
Color:
Typography:
Layout:
Accuracy:
…
围绕客户问题进行多来源调研与溯源，快速整理背景并支持准确回复。
快速生成概念、架构、流程与白板图，支持 SVG、HTML 或 Excalidraw 格式