data-exploration-distribution-analysis
Sub-skill of data-exploration: Distribution Analysis (+3).
Best use case
data-exploration-distribution-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-exploration: Distribution Analysis (+3).
Teams using data-exploration-distribution-analysis 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/distribution-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-exploration-distribution-analysis Compares
| Feature / Agent | data-exploration-distribution-analysis | 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-exploration: Distribution Analysis (+3).
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.
SKILL.md Source
# Distribution Analysis (+3) ## Distribution Analysis For numeric columns, characterize the distribution: - **Normal**: Mean and median are close, bell-shaped - **Skewed right**: Long tail of high values (common for revenue, session duration) - **Skewed left**: Long tail of low values (less common) - **Bimodal**: Two peaks (suggests two distinct populations) - **Power law**: Few very large values, many small ones (common for user activity) - **Uniform**: Roughly equal frequency across range (often synthetic or random) ## Temporal Patterns For time series data, look for: - **Trend**: Sustained upward or downward movement - **Seasonality**: Repeating patterns (weekly, monthly, quarterly, annual) - **Day-of-week effects**: Weekday vs. weekend differences - **Holiday effects**: Drops or spikes around known holidays - **Change points**: Sudden shifts in level or trend - **Anomalies**: Individual data points that break the pattern ## Segmentation Discovery Identify natural segments by: - Finding categorical columns with 3-20 distinct values - Comparing metric distributions across segment values - Looking for segments with significantly different behavior - Testing whether segments are homogeneous or contain sub-segments ## Correlation Exploration Between numeric columns: - Compute correlation matrix for all metric pairs - Flag strong correlations (|r| > 0.7) for investigation - Note: Correlation does not imply causation -- flag this explicitly - Check for non-linear relationships (e.g., quadratic, logarithmic)
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