Files
pm-claude-skills/exports/aider/pm-analytics/product-health-analysis/product-health-analysis.md
T
mohitagw15856 036511ab3e Windsurf + Aider targets, MCP server, and demo placement (#33)
Broadens both reach (more tools) and content types (an MCP server), continuing
the multi-platform story.

Windsurf + Aider:
- build-exports.mjs gains two platforms: exports/windsurf/*.md (workspace rules,
  trigger: model_decision) and exports/aider/*.md (conventions for `aider --read`).
  Now 5 platforms (ChatGPT, Gemini, Cursor, Windsurf, Aider).
- install.sh + bin/cli.mjs install both (windsurf -> .windsurf/rules, aider ->
  .aider/skills with a --read hint); generated README index is excluded from copies.
- One-line windsurf-install.sh / aider-install.sh wrappers for parity.

MCP server (new content type):
- mcp/server.mjs — zero-dependency stdio MCP server exposing list_skills,
  search_skills, get_skill. Published as a second bin (pm-claude-skills-mcp).
  Logs to stderr; reads bundled skills/ at startup. mcp/README.md documents
  client config.

Also: README hero "See it in action" demo placement (ready to swap in a GIF;
recording guide in web/docs-assets/README.md), Works-With table + exports +
install docs updated, CHANGELOG Unreleased. package.json files/bin updated.


Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px

Co-authored-by: Claude <noreply@anthropic.com>
2026-06-17 23:15:38 +01:00

3.1 KiB

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