data-analysis-testing-data-analysis-code
Sub-skill of data-analysis: Testing Data Analysis Code.
Best use case
data-analysis-testing-data-analysis-code is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-analysis: Testing Data Analysis Code.
Teams using data-analysis-testing-data-analysis-code should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/testing-data-analysis-code/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-analysis-testing-data-analysis-code Compares
| Feature / Agent | data-analysis-testing-data-analysis-code | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of data-analysis: Testing Data Analysis Code.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Testing Data Analysis Code
## Testing Data Analysis Code
```python
import pytest
import polars as pl
def test_aggregation_logic():
"""Test aggregation produces expected results."""
test_data = pl.DataFrame({
"category": ["A", "A", "B"],
"value": [100, 200, 150]
})
result = aggregate_by_category(test_data)
assert result.filter(pl.col("category") == "A")["total"][0] == 300
assert result.filter(pl.col("category") == "B")["total"][0] == 150
def test_dashboard_callback():
"""Test dashboard callback returns valid figures."""
from dash.testing.composite import DashComposite
# Test callback outputs are valid plotly figures
main, pie, bar = update_charts("revenue")
assert main.data is not None
assert pie.data is not None
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