mohitagw15856
f3b9d008fe
New skills added: - teaching-lesson-plan: structured lesson plans for any subject/audience/setting - seo-content-brief: complete SEO briefs with intent, competitor gaps, and outline - media-pitch: story-first journalist pitches with angle development framework - change-management-plan: stakeholder analysis, comms strategy, adoption metrics - workshop-facilitation-guide: activity instructions, decision protocols, facilitator moves - sales-forecasting-model: pipeline model, scenario analysis, assumption log - tax-planning-checklist: year-end tax planning across income, pension, CGT, reliefs Quality improvements across all 93 existing skills: - Standardised description format: "Verb the thing. Use when X. Produces Y." - Added Required Inputs section to all skills missing it (prompts for missing info) - Added Quality Checks section to all skills missing it (specific, not generic) - Fixed broken multiline YAML descriptions - Removed non-standard frontmatter keys (tool_integration, metadata blocks) README updated to v6.0.0 with 100-skill count, new skill tables, and article series Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2.9 KiB
2.9 KiB
name, description
| name | description |
|---|---|
| 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. |
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:
- Acquisition — new users, source quality, CAC trends
- Activation — time to first value, onboarding completion rates
- Engagement — DAU/MAU, feature adoption, session depth
- Retention — D1/D7/D30 retention, churn rate, resurrection rate
Process
- For each metric, compare: current period vs. previous period, current vs. target
- Flag anything more than 10% off target as requiring investigation
- Look for correlations — does a drop in activation explain a retention dip 2 weeks later?
- Write a plain-English health summary (no jargon) suitable for sharing with non-data stakeholders
- Recommend top 3 areas for immediate investigation with suggested diagnostic steps
- 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:
- [Metric + hypothesis + suggested diagnostic]
- [Metric + hypothesis + suggested diagnostic]
- [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