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Data Analysis Standard

🟢 Production-Ready🕐 updated 2026-07-14 🔷 SkillSpec L3 pm-analytics

Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.

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What to give it

Metric or question — being investigated
Time period — what changed, from when to when
Data available — which segments, sources, or queries you have access to
Business context — what decision this analysis informs
Audience — who will read this — exec / team / data team

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

Analysis answers all 4 questions: what changed, why, so what, now what
Root cause has evidence (not just hypothesis)
Confidence level is stated and justified
What the data cannot tell us is explicitly named
Recommended action includes an owner and timeline

⚠️ What it refuses to do

Do not present correlations as causation — always state the distinction explicitly
Do not report a metric movement without stating the time window and comparison baseline
Do not skip the "so what" — raw observations without recommended actions are incomplete analysis
Do not overstate confidence — label hypotheses clearly and note what data would be needed to confirm them
Do not ignore segment breakdowns — aggregate metrics can mask opposing trends in sub-segments

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcp

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💬 Discussion

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