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Pm-claude-skills product-health-analysis

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.

install
source · Clone the upstream repo
git clone https://github.com/mohitagw15856/pm-claude-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mohitagw15856/pm-claude-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/product-health-analysis" ~/.claude/skills/mohitagw15856-pm-claude-skills-product-health-analysis-dd8c45 && rm -rf "$T"
manifest: skills/product-health-analysis/SKILL.md
source content

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

MetricCurrentTargetvs. Last PeriodStatus
[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