Agent skill · data analytics · daffy0208
product-analyst
Track user metrics and provide data-driven insights for product decisions. Use when measuring product health, analyzing user behavior, conducting cohort analysis, or optimizing key metrics. Covers acquisition, engagement, retention, revenue metrics, and data-driven decision making.
Why this skill is useful
Provides detailed metrics and analysis frameworks for product health that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2025-12-25. 33 stars on the source repository.
What this skill does
Product Analyst Measure user behavior and product health to inform data-driven decisions. Core Principle What gets measured gets improved. Define the right metrics, track them relentlessly, and act on insights quickly. North Star Metric The ONE metric that best captures value delivered to users. Your North Star should: ✅ Represent real customer value ✅ Correlate with revenue ✅ Be measurable frequently (daily/weekly) ✅ Rally the entire team around one goal Examples by Product Type: How to choose your North Star: 1. What action represents core value? 2. If users do this more, do they get more value? 3. Does this predict revenue? 4. Can the entire team influence it? Key Metrics by Category Acquisition Metrics Goal: Get users into the product Activation Metrics Goal: Get users to "aha moment" Engagement Metrics Goal: Keep users coming back Retention Metrics Goal: Prevent churn Revenue Metrics Goal: Monetize effectively Satisfaction Metrics Goal: Keep customers happy Segmentation Don't treat all users the same. Different cohorts behave differently. Funnel Analysis Track conversion at each stage: Cohort Analysis Compare user groups over time: A/B Testing Test hypotheses systematically: Dashboard Design Executive Dashboard Product Dashboard Marketing Dashboard Tools & Software Reporting Cadence Quick Start Checklist [ ] Define North Star Metric [ ] Set up event tracking (Mixpanel/Amplitude) [ ] Instrument key events (sign-up, activation, core actions) [ ] Create acquisition funnel [ ] Track retention cohorts [ ] Build executive dashboard [ ] Set up weekly reporting [ ] Run first A/B test Common Pitfalls ❌ Vanity metrics: Tracking metrics that look good but don't predict success (e.g., page views) ❌ Too many metrics: Focus on 3-5 key metrics, not 50 ❌ No North Star: Team pulls in different directions ❌ Ignoring segments: Averages hide important patterns ❌ Analysis paralysis: Measure, learn, act quickly ❌ Not acting on data: Data without action is worthless Summary Great pro …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills daffy0208/product-analyst