Mohit
affae033fe
Propagates Anti-Patterns sections, description rewrites, Required Inputs additions, and Quality Checks format fixes from skills/ to matching plugin SKILL.md copies. https://claude.ai/code/session_01MuGKn3a3Gbqoe8uM5Lmuqt
68 lines
3.4 KiB
Markdown
68 lines
3.4 KiB
Markdown
---
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name: product-health-analysis
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description: "Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions."
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---
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# Product Health Analysis Skill
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Transform raw metrics data into a clear health narrative — what's working, what's not, and what needs immediate attention.
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## Required Inputs
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Ask the user for these if not provided:
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- **Metrics data** (current values for key metrics — even rough numbers work)
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- **Targets or benchmarks** (OKR targets, historical baselines, or industry benchmarks)
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- **Period** (week / month / quarter being analysed)
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- **Product area or segment** (are we looking at the whole product or a specific feature?)
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## Metrics Framework
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Analyse across four layers:
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1. **Acquisition** — new users, source quality, CAC trends
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2. **Activation** — time to first value, onboarding completion rates
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3. **Engagement** — DAU/MAU, feature adoption, session depth
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4. **Retention** — D1/D7/D30 retention, churn rate, resurrection rate
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## Process
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1. For each metric, compare: current period vs. previous period, current vs. target
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2. Flag anything more than 10% off target as requiring investigation
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3. Look for correlations — does a drop in activation explain a retention dip 2 weeks later?
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4. Write a plain-English health summary (no jargon) suitable for sharing with non-data stakeholders
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5. Recommend top 3 areas for immediate investigation with suggested diagnostic steps
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6. **Validate** — Confirm every flagged metric has a plausible root cause hypothesis, not just a raw number, and every recommended action has a specific owner or team
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## Output Structure
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### Product Health Report — [Period]
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**Overall Health:** 🟢 On Track / 🟡 Watch / 🔴 Action Required
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| Metric | Current | Target | vs. Last Period | Status |
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|--------|---------|--------|-----------------|--------|
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| [metric] | [value] | [target] | [+/-%] | [🟢/🟡/🔴] |
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**Key Observations:**
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[3-5 bullet observations written in plain English]
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**Areas Requiring Investigation:**
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1. [Metric + hypothesis + suggested diagnostic]
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2. [Metric + hypothesis + suggested diagnostic]
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3. [Metric + hypothesis + suggested diagnostic]
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**Recommended Actions:**
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[Specific next steps with owners and timelines]
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## Quality Checks
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- [ ] Every metric includes both a target and a trend (not just a snapshot)
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- [ ] At least one correlation is drawn between metrics (e.g., activation → retention)
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- [ ] Every flagged metric has a root cause hypothesis, not just "it dropped"
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- [ ] Observations are written for a non-technical stakeholder (no raw query language or data jargon)
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- [ ] Overall health rating is justified with specific evidence
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## Anti-Patterns
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- [ ] Do not report a single aggregate metric without segment breakdowns — averages hide opposing trends
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- [ ] Do not flag a metric as healthy just because it is above the target — check if the target itself is meaningful
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- [ ] Do not list metric movements without root cause hypotheses — observations without explanations are not analysis
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- [ ] Do not mix product health metrics with business KPIs without explaining the relationship between them
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- [ ] Do not omit recommended actions — a health report that only describes problems without prioritised next steps is incomplete
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