Segment Analysis
Discover natural data segments and analyze group differences: clustering-based segmentation, cross-tabulation, statistical comparison between groups, and segment profiling. Use when looking for natural groupings or comparing subpopulations.
Install
$ agentstack add skill-ericwang915-data-scientist-skills-segment-analysis ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ericwang915-data-scientist-skills-segment-analysis) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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How agent discovery & health will work →About
Segment Analysis
Purpose
Discover natural segments in your data and characterize how groups differ. Combines unsupervised clustering with statistical comparison to reveal meaningful subpopulations.
How It Works
Step 1: Identify Segments
- Predefined segments: Analyze existing categories (e.g., by plan tier, region)
- Data-driven segments: Discover groups using K-means, DBSCAN, or hierarchical clustering
- Optimal cluster count via elbow method, silhouette score, gap statistic
Step 2: Profile Each Segment
- Size: count and percentage of total
- Demographics: distribution of key features per segment
- Behavior: mean/median of behavioral metrics per segment
- Distinguishing features: what makes each segment unique
Step 3: Compare Segments
- Statistical tests for group differences (ANOVA, Kruskal-Wallis, chi-squared)
- Effect sizes (Cohen's d, eta-squared)
- Pairwise post-hoc comparisons with multiple testing correction
Step 4: Actionable Insights
- Name each segment with a descriptive label
- Rank segments by business value or analytical interest
- Recommend targeted actions per segment
Usage Examples
"Segment our users based on engagement patterns and tell me
how each segment differs in conversion rate""Compare feature usage across free, pro, and enterprise tiers —
which features drive upgrades?"Output Format
- Segment Profiles: Descriptive summary of each segment
- Comparison Table: Side-by-side metrics with statistical significance
- Visualizations: Segment distributions, radar charts, parallel coordinates
- Python Code: Segmentation and profiling script
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ericwang915
- Source: ericwang915/data-scientist-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.