product-health-analysis
Product Health Analysis interprets product metrics against established goals to identify performance gaps and opportunities. Use this skill when analyzing overall product health, reviewing key performance indicators, investigating specific performance issues, generating health reports, or assessing product-market fit signals. It produces a structured report with RAG status indicators, trend analysis across acquisition, activation, engagement, and retention layers, root cause hypotheses for flagged metrics, and prioritized actions with suggested diagnostics.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/product-health-analysis && cp -r /tmp/product-health-analysis/plugins/pm-analytics/skills/product-health-analysis ~/.claude/skills/product-health-analysisSKILL.md
# Product Health Analysis Skill Transform raw metrics data into a clear health narrative — what's working, what's not, and what needs immediate attention. ## Required Inputs Ask the user for these if not provided: - **Metrics data** (current values for key metrics — even rough numbers work) - **Targets or benchmarks** (OKR targets, historical baselines, or industry benchmarks) - **Period** (week / month / quarter being analysed) - **Product area or segment** (are we looking at the whole product or a specific feature?) ## Metrics Framework Analyse across four layers: 1. **Acquisition** — new users, source quality, CAC trends 2. **Activation** — time to first value, onboarding completion rates 3. **Engagement** — DAU/MAU, feature adoption, session depth 4. **Retention** — D1/D7/D30 retention, churn rate, resurrection rate ## Process 1. For each metric, compare: current period vs. previous period, current vs. target 2. Flag anything more than 10% off target as requiring investigation 3. Look for correlations — does a drop in activation explain a retention dip 2 weeks later? 4. Write a plain-English health summary (no jargon) suitable for sharing with non-data stakeholders 5. Recommend top 3 areas for immediate investigation with suggested diagnostic steps 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 ## Output Structure ### Product Health Report — [Period] **Overall Health:** 🟢 On Track / 🟡 Watch / 🔴 Action Required | Metric | Current | Target | vs. Last Period | Status | |--------|---------|--------|-----------------|--------| | [metric] | [value] | [target] | [+/-%] | [🟢/🟡/🔴] | **Key Observations:** [3-5 bullet observations written in plain English] **Areas Requiring Investigation:** 1. [Metric + hypothesis + suggested diagnostic] 2. [Metric + hypothesis + suggested diagnostic] 3. [Metric + hypothesis + suggested diagnostic] **Recommended Actions:** [Specific next steps with owners and timelines] ## Quality Checks - [ ] Every metric includes both a target and a trend (not just a snapshot) - [ ] At least one correlation is drawn between metrics (e.g., activation → retention) - [ ] Every flagged metric has a root cause hypothesis, not just "it dropped" - [ ] Observations are written for a non-technical stakeholder (no raw query language or data jargon) - [ ] Overall health rating is justified with specific evidence ## Anti-Patterns - [ ] Do not report a single aggregate metric without segment breakdowns — averages hide opposing trends - [ ] Do not flag a metric as healthy just because it is above the target — check if the target itself is meaningful - [ ] Do not list metric movements without root cause hypotheses — observations without explanations are not analysis - [ ] Do not mix product health metrics with business KPIs without explaining the relationship between them - [ ] Do not omit recommended actions — a health report that only describes problems without prioritised next steps is incomplete
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