What do I need to use Data Analysis Standard?

Metric or question; Time period; Data available; Business context; Audience.

Is Data Analysis Standard free to use?

Yes — run it free in the browser playground (no API key needed for the sponsored trial, or bring any provider key), or install it into Claude Code, Cursor, ChatGPT and more via npx pm-claude-skills. MIT-licensed and open source.

Data Analysis Standard

Q: How do I know the Data Analysis Standard output is good?

The skill verifies itself against its own quality checks: 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.

🟢 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.

▶ Run it free — no key needed 📝 Grade your existing draft View SKILL.md ↗

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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