mohitagw15856
036511ab3e
Broadens both reach (more tools) and content types (an MCP server), continuing the multi-platform story. Windsurf + Aider: - build-exports.mjs gains two platforms: exports/windsurf/*.md (workspace rules, trigger: model_decision) and exports/aider/*.md (conventions for `aider --read`). Now 5 platforms (ChatGPT, Gemini, Cursor, Windsurf, Aider). - install.sh + bin/cli.mjs install both (windsurf -> .windsurf/rules, aider -> .aider/skills with a --read hint); generated README index is excluded from copies. - One-line windsurf-install.sh / aider-install.sh wrappers for parity. MCP server (new content type): - mcp/server.mjs — zero-dependency stdio MCP server exposing list_skills, search_skills, get_skill. Published as a second bin (pm-claude-skills-mcp). Logs to stderr; reads bundled skills/ at startup. mcp/README.md documents client config. Also: README hero "See it in action" demo placement (ready to swap in a GIF; recording guide in web/docs-assets/README.md), Works-With table + exports + install docs updated, CHANGELOG Unreleased. package.json files/bin updated. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
130 lines
4.5 KiB
Markdown
130 lines
4.5 KiB
Markdown
# Data Analysis Standard Skill
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Turn raw numbers into product decisions. Structure every analysis with a clear question, methodology, finding, and recommended action.
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## Analysis Framework: The 4-Question Method
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Every analysis starts here:
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1. **What changed?** (describe the metric and its movement)
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2. **Why did it change?** (root cause — segment, funnel step, cohort, channel)
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3. **So what?** (business or product impact)
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4. **Now what?** (recommended action with confidence level)
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Never deliver data without answering all four. A chart with no narrative is not an analysis.
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---
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## Metric Triage Template
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Use when a metric has moved unexpectedly:
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```
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METRIC: [Name]
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MOVEMENT: [X% change over Y period]
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BASELINE: [What was normal]
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SEGMENTATION CHECK:
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- By platform (iOS / Android / Web)?
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- By user cohort (new / returning / power users)?
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- By acquisition channel?
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- By geography?
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- By plan/tier?
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ROOT CAUSE HYPOTHESIS:
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1. [Most likely explanation] — Evidence: [data point]
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2. [Alternative explanation] — Evidence: [data point]
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3. [Ruling out] — Eliminated because: [reason]
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CONCLUSION: [Single sentence answer to "why did this change?"]
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CONFIDENCE: [High / Medium / Low] — based on [data available]
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```
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---
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## Funnel Analysis Structure
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| Stage | Metric | Current | Benchmark/Target | Drop-off % | Notes |
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|---|---|---|---|---|---|
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| [Top of funnel] | [Users] | [N] | [N] | — | |
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| [Step 2] | [Users] | [N] | [N] | [X%] | |
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| [Step 3] | [Users] | [N] | [N] | [X%] | |
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| [Conversion] | [Users] | [N] | [N] | [X%] | |
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**Biggest drop-off:** [Step X → Step Y] — Hypothesis: [reason]
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**Recommended investigation:** [specific query or test]
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---
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## Cohort Analysis Guidelines
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Always define:
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- **Cohort definition:** [What groups users — signup week, first action, plan type]
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- **Retention metric:** [What counts as retained — login, core action, revenue]
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- **Retention window:** [D1, D7, D30, W4, M3, etc.]
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Output a cohort retention table and annotate:
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- Baseline retention for each cohort
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- Cohorts that over/underperform and why (feature launch? campaign? seasonal?)
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- Trend direction across cohorts (improving / declining / stable)
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---
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## Stakeholder Analysis Output Format
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### [Analysis Title] — [Date]
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**Question being answered:** [Specific question in plain English]
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**Time period:** [Date range]
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**Data source:** [Where data comes from]
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**Finding:**
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> [1–2 sentence plain-English summary of what the data shows]
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**Key chart / table:** [Include or describe]
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**Root cause:** [Best explanation with evidence]
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**Confidence level:** [High / Medium / Low] — [reason]
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**Recommended action:**
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1. [Immediate action — owner, timeline]
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2. [Investigation needed — what to check next]
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3. [Monitoring — what metric to watch and at what cadence]
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**What this analysis does NOT tell us:** [Important caveat — what data is missing or what can't be concluded]
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---
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## Required Inputs
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Ask the user for these if not provided:
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- **Metric or question** being investigated
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- **Time period** (what changed, from when to when)
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- **Data available** (which segments, sources, or queries you have access to)
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- **Business context** (what decision this analysis informs)
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- **Audience** (who will read this — exec / team / data team)
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## Quality Checks
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- [ ] Analysis answers all 4 questions: what changed, why, so what, now what
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- [ ] Root cause has evidence (not just hypothesis)
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- [ ] Confidence level is stated and justified
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- [ ] What the data cannot tell us is explicitly named
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- [ ] Recommended action includes an owner and timeline
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## Anti-Patterns
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- [ ] Do not present correlations as causation — always state the distinction explicitly
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- [ ] Do not report a metric movement without stating the time window and comparison baseline
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- [ ] Do not skip the "so what" — raw observations without recommended actions are incomplete analysis
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- [ ] Do not overstate confidence — label hypotheses clearly and note what data would be needed to confirm them
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- [ ] Do not ignore segment breakdowns — aggregate metrics can mask opposing trends in sub-segments
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## Guidelines
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- Always state what the data *cannot* tell you — never oversell confidence
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- Correlations are not causation — flag this every time
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- If the user has no baseline, recommend establishing one before drawing conclusions
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- Recommend the simplest chart for each finding: bar for comparison, line for trends, scatter for correlation, table for detailed breakdowns
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- Always specify the time window — "conversion dropped" is meaningless without "from X to Y over Z period"
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