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Data Analysis

Converts data questions into decision-ready analysis with metric contracts and statistical validation.

4|Updated Mar 21, 2026
Search Tags:#data analysis#sql#visualization#cohort analysis#kpi#metric contracts#decision brief
One-click install
npx skills add https://github.com/alexhegit/sovereign-IQ --skill data-analysis-alexhegit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Data Analysis
Source: https://github.com/alexhegit/sovereign-IQ/tree/main/workspace/skills/data-analysis
Command: npx skills add https://github.com/alexhegit/sovereign-IQ --skill data-analysis-alexhegit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you turn raw data into decision-ready insights by enforcing metric definitions, choosing the right visualizations, and communicating uncertainty and caveats clearly.

Core Features & Use Cases

  • Metric contract discipline: Define grain, numerator/denominator, filters, time window, timezone, and source-of-truth before calculating or comparing KPIs.
  • Statistical rigor and validation: Apply checks for sample size, fair baselines, multiple comparisons, effect size vs p-value, and uncertainty ranges.
  • Decision-brief outputs: Produce stakeholder-ready summaries (answer, evidence, confidence, caveats, and next action) instead of dumping raw findings.
  • Chart selection guidance: Select visuals based on the decision question (trend, comparison, distribution, relationship, funnel, cohort retention) and avoid common anti-patterns.

Quick Start

Use the Data Analysis skill to analyze your KPI discrepancy by first stating the decision question and the metric contract, then requesting a decision brief with the right chart and uncertainty.

Frequently Asked Questions about Data Analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a decision brief from raw SQL data?▼

To create a decision brief from raw SQL data, first define the metric contract specifying grain, filters, and time window, then separate data extraction from interpretation to quantify uncertainty and produce a stakeholder-ready summary with caveats and next steps.

What is a metric contract and when do I need it for KPI analysis?▼

A metric contract defines the grain, numerator/denominator, filters, time window, timezone, and source-of-truth before calculating KPIs. You need it for KPI analysis to ensure valid comparisons and prevent metric discrepancies during anomaly reviews.

How do I choose the right visualization for cohort analysis and funnel data?▼

Choose the right visualization for cohort analysis and funnel data by matching the chart to the decision question, selecting trend, comparison, distribution, relationship, funnel, or cohort retention visuals while avoiding common chart anti-patterns.

Can I use this approach for experiment readouts and executive reporting across spreadsheets and notebooks?▼

Yes, you can apply this analysis approach to experiment readouts and executive reporting across SQL, spreadsheets, notebooks, and dashboards by enforcing statistical rigor checks for sample size, fair baselines, and multiple comparisons before computation.

What is the best way to structure a decision brief for stakeholders?▼

The best way to structure a decision brief is to produce a stakeholder-ready summary containing the answer, supporting evidence, confidence level, caveats, and the next action, separating raw findings from the final interpretation.

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