product-health-analysis

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