data-analysis

name	Data Analysis
slug	data-analysis
version	1.0.2
homepage	https://clawic.com/skills/data-analysis
description	Data analysis and visualization. Query databases, generate reports, automate spreadsheets, and turn raw data into clear, actionable insights. Use when (1) you need to analyze, visualize, or explain data; (2) the user wants reports, dashboards, or metrics turned into a decision; (3) the work involves SQL, Python, spreadsheets, BI tools, or notebooks; (4) you need to compare segments, cohorts, funnels, experiments, or time periods; (5) the user explicitly installs or references the skill for the current task.
changelog	Added metric contracts, chart guidance, and decision brief templates for more reliable analysis.
metadata	{"clawdbot":{"emoji":"D","requires":{"bins":[]},"os":["linux","darwin","win32"]}}

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