Data Analysis
Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls.
Best use case
Data Analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls.
Teams using Data 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/data-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Data Analysis Compares
| Feature / Agent | Data 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?
Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls.
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.
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SKILL.md Source
## When to Load
User asks about: analyzing data, finding patterns, understanding metrics, testing hypotheses, cohort analysis, A/B testing, churn analysis, statistical significance.
## Core Principle
Analysis without a decision is just arithmetic. Always clarify: **What would change if this analysis shows X vs Y?**
## Methodology First
Before touching data:
1. **What decision** is this analysis supporting?
2. **What would change your mind?** (the real question)
3. **What data do you actually have** vs what you wish you had?
4. **What timeframe** is relevant?
## Statistical Rigor Checklist
- [ ] Sample size sufficient? (small N = wide confidence intervals)
- [ ] Comparison groups fair? (same time period, similar conditions)
- [ ] Multiple comparisons? (20 tests = 1 "significant" by chance)
- [ ] Effect size meaningful? (statistically significant ≠ practically important)
- [ ] Uncertainty quantified? ("12-18% lift" not just "15% lift")
## Analytical Pitfalls to Catch
| Pitfall | What it looks like | How to avoid |
|---------|-------------------|--------------|
| Simpson's Paradox | Trend reverses when you segment | Always check by key dimensions |
| Survivorship bias | Only analyzing current users | Include churned/failed in dataset |
| Comparing unequal periods | Feb (28d) vs March (31d) | Normalize to per-day or same-length windows |
| p-hacking | Testing until something is "significant" | Pre-register hypotheses or adjust for multiple comparisons |
| Correlation in time series | Both went up = "related" | Check if controlling for time removes relationship |
| Aggregating percentages | Averaging percentages directly | Re-calculate from underlying totals |
For detailed examples of each pitfall, see `pitfalls.md`.
## Approach Selection
| Question type | Approach | Key output |
|---------------|----------|------------|
| "Is X different from Y?" | Hypothesis test | p-value + effect size + CI |
| "What predicts Z?" | Regression/correlation | Coefficients + R² + residual check |
| "How do users behave over time?" | Cohort analysis | Retention curves by cohort |
| "Are these groups different?" | Segmentation | Profiles + statistical comparison |
| "What's unusual?" | Anomaly detection | Flagged points + context |
For technique details and when to use each, see `techniques.md`.
## Output Standards
1. **Lead with the insight**, not the methodology
2. **Quantify uncertainty** — ranges, not point estimates
3. **State limitations** — what this analysis can't tell you
4. **Recommend next steps** — what would strengthen the conclusion
## Red Flags to Escalate
- User wants to "prove" a predetermined conclusion
- Sample size too small for reliable inference
- Data quality issues that invalidate analysis
- Confounders that can't be controlled forRelated Skills
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