name: viz description: > Create publication-quality interactive visualizations from data files. Use when user asks to plot, chart, graph, or visualize data. Supports matplotlib/seaborn with interactive display and PNG output. allowed-tools: > Read, Write(.viz/), Glob(.viz/), Bash(uv run --project {SKILL_DIR}/scripts python {SKILL_DIR}/scripts/viz_runner.py *), Bash

Viz Skill

Data Requirement

All data must live in .viz/. Before plotting, always copy or generate the source data into .viz/<name>.parquet (or .csv), even if the original is a local file. This makes every visualization fully self-contained and reproducible.

Workflow

1. Materialize data — Copy or generate data into .viz/<name>.parquet (or .csv)
2. Check for collisions — Verify no existing .viz/<name>.py conflicts with your chosen name
3. Write script — Use the Write tool to create .viz/<name>.py
4. Execute — Run via the viz runner
5. Report — Confirm the output path .viz/<name>.png to the user

File Convention

Every visualization is a name-prefix triplet inside .viz/:

Names must be lowercase_snake_case. All paths inside scripts are relative — the runner cds into .viz/ before execution.

Script Generation

Every generated script must include these sections in order:

1. Imports — All imports inside the script (self-contained execution)
2. Data loading — Hardcoded: pd.read_parquet('<name>.parquet') or pd.read_csv('<name>.csv')
3. Plot creation — matplotlib/seaborn, publication quality
4. Watermark — Small semi-transparent name label at bottom-right
5. Save — plt.savefig('<name>.png', dpi=150, bbox_inches='tight')
6. Show — plt.show() as the last line

Example Script

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

df = pd.read_parquet('monthly_sales.parquet')

fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(data=df, x='month', y='revenue', ax=ax)
ax.set_title('Monthly Revenue', fontsize=14)
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Revenue ($)', fontsize=12)
ax.grid(axis='y', alpha=0.3)
fig.tight_layout()

fig.text(0.99, 0.01, 'monthly_sales', transform=fig.transFigure,
         fontsize=7, color='gray', alpha=0.3, ha='right', va='bottom')

plt.savefig('monthly_sales.png', dpi=150, bbox_inches='tight')
plt.show()

Runner Usage

Create a visualization:

uv run --project {SKILL_DIR}/scripts python {SKILL_DIR}/scripts/viz_runner.py <name>

Update an existing visualization (overwrites the PNG):

uv run --project {SKILL_DIR}/scripts python {SKILL_DIR}/scripts/viz_runner.py <name> --overwrite

List all existing visualizations:

uv run --project {SKILL_DIR}/scripts python {SKILL_DIR}/scripts/viz_runner.py --list

Efficiency Tips

• Parallelize independent Write calls (e.g., writing the data-generation script and the plot script at the same time when they are independent).
• Chain data generation with the runner in a single Bash call where possible:

  python -c "import shutil; shutil.copy('/path/to/source.parquet', '.viz/sales.parquet')" && uv run --project {SKILL_DIR}/scripts python {SKILL_DIR}/scripts/viz_runner.py sales
  

• For simple data copies, combine into one Bash invocation. Write both scripts in parallel, then chain execution sequentially.

Styling

For publication-quality guidance — font sizes, figure sizes, colorblind-friendly palettes, and when to choose seaborn vs matplotlib — see {SKILL_DIR}/references/styling.md.

Refinement

To modify an existing plot, use the read-modify-overwrite pattern:

1. Read .viz/<name>.py
2. Modify the script
3. Overwrite .viz/<name>.py (using the Write tool)
4. Run: uv run --project {SKILL_DIR}/scripts python {SKILL_DIR}/scripts/viz_runner.py <name> --overwrite

For extended iteration (5+ rounds of refinement), consider spawning a subagent to keep the main conversation clean.