Data Analysis

SKILL.md 节选

## When to Use

Use this skill when the user needs to analyze, explain, or visualize data from SQL, spreadsheets, notebooks, dashboards, exports, or ad hoc tables.

Use it for KPI debugging, experiment readouts, funnel or cohort analysis, anomaly reviews, executive reporting, and quality checks on metrics or query logic.

Prefer this skill over generic coding or spreadsheet help when the hard part is analytical judgment: metric definition, comparison design, interpretation, or recommendation.

User asks about: analyzing data, finding patterns, understanding metrics, testing hypotheses, cohort analysis, A/B testing, churn analysis, or 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")

## Architecture

This skill does not require local folders, persistent memory, or setup state.

Use the included reference files as lightweight guides:
- `metric-contracts.md` for KPI definitions and caveats
- `chart-selection.md` for visual choice and chart anti-patterns
- `decision-briefs.md` for stakeholder-facing outputs
- `pitfalls.md` and `techniques.md` for analytical rigor and method choice

## Quick Reference

Load only the smallest relevant file to keep context focused.

| Topic | File |
|-------|------|
| Metric definition contracts | `metric-contracts.md` |
| Visual selection and chart anti-patterns | `chart-selection.md` |
| Decision-ready output formats | `decision-briefs.md` |
| Failure modes to catch early | `pitfalls.md` |
| Method selection by question type | `techniques.md` |

## Core Rules

### 1. Start from the decision, not the dataset
- Identify the decision owner, the question that could change a decision, and the deadline before doing analysis.
- If no decision would change, reframe the request before computing anything.

### 2. Lock the metric contract before calculating
- Define entity, grain, numerator, denominator, time window, timezone, filters, exclusions, and source of truth.
- If any of those are ambiguous, state the ambiguity explicitly before presenting results.

### 3. Separate extraction, transformation, and interpretation
- Keep query logic, cleanup assumptions, and analytical conclusions distinguishable.
- Never hide business assumptions inside SQL, formulas, or notebook code without naming them in the write-up.

### 4. Choose visuals to answer a question
- Select charts based on the analytical question: trend, comparison, distribution, relationship, composition, funnel, or cohort retention.
- Do not add charts that make the deck look fuller but do not change the decision.

### 5. Brief every result in decision format
- Every output should include the answer, evidence, confidence, caveats, and recommended next action.
- If the output is going to a stakeholder, translate the method into business implications instead of leading with technical detail.

### 6. Stress-test claims before recommending action
- Segment by obvious confounders, compare the right baseline, quantify uncertainty, and check sensitivity to exclusions or time windows.
- Strong-looking numbers without robustness checks are not decision-ready.

### 7. Escalate when the data cannot support the claim
- Block or downgrade conclusions when sample size is weak, the source is unreliable, definitions drifted, or confounding is unresolved.
- It is better to say "unknown yet" than to produce false confidence.

SKILL.md 不止这些内容，安装后可阅读全文。

技能包内容

共 7 个文件，合计 21.0 KiB