New 15 more skills

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+---
+name: data-analysis-standard
+description: Structures product data analysis, metric deep-dives, funnel analysis, and cohort studies. Use when asked to analyse product metrics, investigate a drop in conversion, build a dashboard spec, or explain data to stakeholders. Triggers on "analyse metrics", "funnel analysis", "cohort analysis", "data deep dive", "why did X drop".
+---
+
+# Data Analysis Standard Skill
+
+Turn raw numbers into product decisions. Structure every analysis with a clear question, methodology, finding, and recommended action.
+
+## Analysis Framework: The 4-Question Method
+
+Every analysis starts here:
+1. **What changed?** (describe the metric and its movement)
+2. **Why did it change?** (root cause — segment, funnel step, cohort, channel)
+3. **So what?** (business or product impact)
+4. **Now what?** (recommended action with confidence level)
+
+Never deliver data without answering all four. A chart with no narrative is not an analysis.
+
+---
+
+## Metric Triage Template
+
+Use when a metric has moved unexpectedly:
+
+```
+METRIC: [Name]
+MOVEMENT: [X% change over Y period]
+BASELINE: [What was normal]
+
+SEGMENTATION CHECK:
+- By platform (iOS / Android / Web)?
+- By user cohort (new / returning / power users)?
+- By acquisition channel?
+- By geography?
+- By plan/tier?
+
+ROOT CAUSE HYPOTHESIS:
+1. [Most likely explanation] — Evidence: [data point]
+2. [Alternative explanation] — Evidence: [data point]
+3. [Ruling out] — Eliminated because: [reason]
+
+CONCLUSION: [Single sentence answer to "why did this change?"]
+CONFIDENCE: [High / Medium / Low] — based on [data available]
+```
+
+---
+
+## Funnel Analysis Structure
+
+| Stage | Metric | Current | Benchmark/Target | Drop-off % | Notes |
+|---|---|---|---|---|---|
+| [Top of funnel] | [Users] | [N] | [N] | — | |
+| [Step 2] | [Users] | [N] | [N] | [X%] | |
+| [Step 3] | [Users] | [N] | [N] | [X%] | |
+| [Conversion] | [Users] | [N] | [N] | [X%] | |
+
+**Biggest drop-off:** [Step X → Step Y] — Hypothesis: [reason]
+**Recommended investigation:** [specific query or test]
+
+---
+
+## Cohort Analysis Guidelines
+
+Always define:
+- **Cohort definition:** [What groups users — signup week, first action, plan type]
+- **Retention metric:** [What counts as retained — login, core action, revenue]
+- **Retention window:** [D1, D7, D30, W4, M3, etc.]
+
+Output a cohort retention table and annotate:
+- Baseline retention for each cohort
+- Cohorts that over/underperform and why (feature launch? campaign? seasonal?)
+- Trend direction across cohorts (improving / declining / stable)
+
+---
+
+## Stakeholder Analysis Output Format
+
+### [Analysis Title] — [Date]
+
+**Question being answered:** [Specific question in plain English]
+**Time period:** [Date range]
+**Data source:** [Where data comes from]
+
+**Finding:**
+> [1–2 sentence plain-English summary of what the data shows]
+
+**Key chart / table:** [Include or describe]
+
+**Root cause:** [Best explanation with evidence]
+
+**Confidence level:** [High / Medium / Low] — [reason]
+
+**Recommended action:**
+1. [Immediate action — owner, timeline]
+2. [Investigation needed — what to check next]
+3. [Monitoring — what metric to watch and at what cadence]
+
+**What this analysis does NOT tell us:** [Important caveat — what data is missing or what can't be concluded]
+
+---
+
+## Guidelines
+
+- Always state what the data *cannot* tell you — never oversell confidence
+- Correlations are not causation — flag this every time
+- If the user has no baseline, recommend establishing one before drawing conclusions
+- Recommend the simplest chart for each finding: bar for comparison, line for trends, scatter for correlation, table for detailed breakdowns
+- Always specify the time window — "conversion dropped" is meaningless without "from X to Y over Z period"