product-analyst — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited product-analyst (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Strategic product analytics expertise for data-driven product decisions — from metrics framework selection to experimentation design and impact measurement.
Great product analytics isn't about tracking everything. It's about measuring what matters to drive better product decisions.
The best product analytics:
When invoked, apply the guidelines in rules/ organized by:
metrics-* — Frameworks (AARRR, HEART), KPI selection, metric hierarchiesfunnel-* — Conversion analysis, drop-off diagnosis, optimizationcohort-* — Retention analysis, segmentation, lifecycle trackingfeature-* — Adoption tracking, usage patterns, feature successexperiment-* — A/B testing, hypothesis design, statistical rigorinstrumentation-* — Event tracking, data modeling, collection best practicesdashboard-* — Visualization, stakeholder reporting, self-serve analytics| Stage | Question | Key Metrics |
|---|---|---|
| Acquisition | Where do users come from? | Traffic sources, CAC, signup rate |
| Activation | Do they have a great first experience? | Time-to-value, setup completion, aha moment |
| Retention | Do they come back? | DAU/MAU, D1/D7/D30 retention, churn |
| Revenue | Do they pay? | Conversion rate, ARPU, LTV |
| Referral | Do they tell others? | NPS, referral rate, viral coefficient |
| Dimension | Definition | Signal Types |
|---|---|---|
| Happiness | User attitudes, satisfaction | NPS, CSAT, surveys |
| Engagement | Depth of involvement | Sessions, time-in-app, actions/session |
| Adoption | New users/features uptake | New users, feature adoption % |
| Retention | Continued usage over time | Retention curves, churn rate |
| Task Success | Efficiency and completion | Task completion, error rate, time-on-task |
┌─────────────────┐
│ North Star │ ← Single metric that matters most
│ Metric │
├─────────────────┤
│ Primary │ ← 3-5 key performance indicators
│ KPIs │
├─────────────────┤
│ Supporting │ ← Diagnostic and health metrics
│ Metrics │
├─────────────────┤
│ Operational │ ← Day-to-day tracking
│ Metrics │
└─────────────────┘┌───────────────────────────────────────────────────────────┐
│ RETENTION VIEWS │
├───────────────────────────────────────────────────────────┤
│ N-Day Retention │ % who return on exactly day N │
│ Unbounded │ % who return on or after day N │
│ Bracket Retention │ % who return within a time window │
│ Rolling Retention │ % still active after N days │
└───────────────────────────────────────────────────────────┘| Level | Approach | When to Use |
|---|---|---|
| 1. Gut | Ship and hope | Never for important features |
| 2. Qualitative | User research, feedback | Early exploration |
| 3. Observational | Pre/post analysis | Low-risk changes |
| 4. Quasi-experiment | Cohort comparison | When randomization hard |
| 5. A/B Test | Randomized control | Optimization, validation |
| 6. Multi-arm Bandit | Adaptive allocation | When speed > precision |
| Criterion | Question | Good Sign |
|---|---|---|
| Actionable | Can we influence this? | Direct lever exists |
| Accessible | Can we measure it reliably? | <5% missing data |
| Auditable | Can we debug anomalies? | Clear calculation logic |
| Aligned | Does it tie to business value? | Executive cares |
| Attributable | Can we trace changes to causes? | A/B testable |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.