# Product Health Diagnostic

> Analyze product health across acquisition, activation, engagement, retention, quality, and monetization.

- Skill: `danielpradilla/product-health-diagnostic` (Agent Skill)
- Install (CLI): `npx skillmds add danielpradilla/product-health-diagnostic`
- Raw SKILL.md: https://api.skillmd.com/api/skills/danielpradilla/product-health-diagnostic/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: danielpradilla (https://skillmd.com/u/danielpradilla)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/danielpradilla/product-health-diagnostic

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# Product Health Diagnostic

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