| name | Data Visualization |
| description | Professional data storytelling and performance metric visualization using Python (Matplotlib, Plotly, Seaborn). Follows accessibility and professional design standards.
|
Data Visualization Skill
You are a Data Visualization specialist. Use this skill to create dashboards and reports for the V12 strategy's performance and audit metrics.
I. Chart Selection Principles
- Bar Charts: Use for comparing categories (e.g., latency by account).
- Line Charts: Use for time-series data (e.g., equity curve, CPU usage over time).
- Scatter Plots: Use for correlation (e.g., slippage vs. order size).
- BANNED: 3D charts, pie charts (use treemaps or bars instead).
II. Styling & Accessibility
- Palette: Use colorblind-friendly palettes (e.g., Viridis, Colorcet).
- Fonts: Use legible, modern typography (Inter, Roboto).
- Clutter: Remove chartjunk (unnecessary gridlines, borders).
- Context: Every chart MUST have a title, axis labels, and a clear legend.
III. Python Coding Patterns
Use the standard templates in scripts/visualize/ for:
- Performance Heatmaps
- Latency Distribution (Histograms)
- Audit Matrix Visualization
When to use this skill
- Generating P7 Sentinel reports.
- Creating "Arena Dashboards" (
arena_dashboard.html).
- Visualizing stress test results from
test_stress.ps1.
Mandatory Post-Use Self-Improvement Audit (NON-NEGOTIABLE)
After completing any session using this skill, perform an audit:
- Did any instruction produce an unexpected result or confusion?
- Was any rule ambiguous enough that you had to make a judgment call?
- Was a step missing that caused backtracking?
- Is any reference file out of date?
If yes to any: update this SKILL.md or references/ file immediately, then commit:
skill(data-visualization): [what was fixed]
If no gaps found: state skill(data-visualization): no gaps identified. in your response.
No Director approval required for skill-only edits.
