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From archora-research
Generates complete, runnable matplotlib/seaborn Python scripts for quantitative data visualizations and Mermaid diagrams for conceptual relationships, workflows, and taxonomies.
npx claudepluginhub richard-kim-79/archora-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/archora-research:figureThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate publication-quality visualization code for research figures.
Creates matplotlib charts with consistent color palettes, saving scripts and PNG outputs to structured subdirectories under a configurable base path.
Generates publication-quality Python data visualizations for research papers using matplotlib, seaborn, numpy, pandas, and top-journal color schemes like Nature/Science.
Generates Mermaid diagrams for flowcharts, sequence diagrams, state diagrams, class diagrams, ERDs, and system architectures to visualize complex concepts and processes.
Share bugs, ideas, or general feedback.
Generate publication-quality visualization code for research figures.
| Type | Use for | Format |
|---|---|---|
| matplotlib | Quantitative data: bar charts, scatter plots, line graphs, heatmaps, box plots | Python |
| seaborn | Statistical visualizations: distributions, regressions, pair plots | Python |
| Mermaid | Conceptual diagrams: workflows, taxonomies, hierarchies, timelines | Mermaid |
## Figure 1: [Title]
**Caption:** [Full figure caption as it would appear in a paper — what is shown and the key takeaway]
**Description:** [What this figure shows and why it matters for the research]
**Type:** Python (matplotlib/seaborn)
```python
import matplotlib.pyplot as plt
import numpy as np
# [Complete, runnable code with realistic placeholder data]
plt.tight_layout()
plt.savefig('figure1.png', dpi=300)
plt.show()
```
your_data_heredpi=300, proper axis labels, legend, titleBefore presenting code to the user, validate syntax with Python's AST parser:
python -c "import ast; ast.parse(open('figure1.py').read()); print('✅ Syntax OK')"
If the check fails, fix the syntax error and re-validate before showing the result.
For inline code blocks, validate with:
import ast
code = """
# paste generated code here
"""
try:
ast.parse(code)
print("✅ Syntax OK")
except SyntaxError as e:
print(f"❌ Syntax error: {e}")
Note: AST validation checks syntax only — it does not catch runtime errors (e.g. wrong data shapes). Always include comments explaining how to adapt placeholder data to real data.
Python figures:
pip install matplotlib seaborn numpy
python figure1.py
Mermaid diagrams: