القائمة
Create publication-quality visualizations with Python
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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Strategic scientific problem selection, project ideation, and troubleshooting based on the Fischbach & Walsh framework.
Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
A conversational framework for systematic scientific problem selection, project ideation, troubleshooting, and strategic decision making.
| name | data-create-viz |
| description | Create publication-quality visualizations with Python |
| requires | ["python"] |
If you see unfamiliar placeholders or need to check which tools are connected, please ask about available integrations.
Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.
You can ask to create a visualization from a data source (e.g., "Create a line chart of monthly revenue" or "Visualize this data").
data source — Query results, pasted data, CSV/Excel file, or data to be queriedchart type — (Optional) Explicit chart type (e.g., "bar chart", "heatmap")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: