Product Analyst
Install this when you need structured product analytics—from North Star and funnels through cohorts, experiments, and dashboards—without guessing which framework applies.
Overview
Product-analyst is an agent skill most often used in Grow (also Validate, Ship) that teaches metrics frameworks, funnels, cohorts, experiments, instrumentation, and dashboard design for solo builders.
Install
npx skills add https://github.com/ncklrs/startup-os-skills --skill product-analystWhat is this skill?
- Seven impact-ranked domains: metrics, funnel, cohort, feature analytics, experimentation, instrumentation, and dashboard
- CRITICAL coverage for AARRR/HEART-style KPI hierarchies, funnel drop-offs, and retention/churn cohorts
- HIGH-impact guidance on A/B test design, event taxonomy, and stakeholder-facing product dashboards
- Lifecycle segmentation and feature adoption depth for feature-level ship/grow decisions
- Self-contained frameworks for North Star definition and metric hierarchy selection
- 7 impact-ranked analytics domains
- 3 CRITICAL areas: metrics, funnel, and cohort/retention
Adoption & trust: 1 installs on skills.sh; 27 GitHub stars; 3/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You are shipping features and spending on growth but lack a coherent metric hierarchy, funnel diagnosis, or retention story to decide what to fix next.
Who is it for?
Solo founders with early or growing user data who need North Star, funnel, cohort, and experiment rigor without hiring a full-time product analyst.
Skip if: Teams that only need a one-off SQL query, pure Salesforce admin work, or frontend UI polish with no measurement plan.
When should I use this skill?
You need to define KPIs, analyze funnels or cohorts, plan experiments, instrument events, or design product dashboards.
What do I get? / Deliverables
You leave with prioritized analytics frameworks and analysis patterns aligned to AARRR-style growth, cohort retention, and experiment interpretation so the next build or marketing cycle targets measurable bottlenecks.
- Metric and North Star recommendations
- Funnel or cohort analysis framing
- Experiment or instrumentation guidance
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Grow/analytics is the canonical shelf because the skill centers on measuring retention, conversion, and feature impact after users exist; instrumentation sections also support earlier setup. Analytics subphase matches metrics frameworks, funnel and cohort analysis, experimentation, and dashboard design—the core outputs product-led solo builders need to compound usage.
Where it fits
Define measurable success criteria and KPI hierarchy before committing to an MVP scope.
Draft an event taxonomy and instrumentation plan while wiring analytics into the app.
Run funnel and cohort reviews to find where activated users leak before scaling spend.
Frame launch experiments with hypotheses and statistical checks instead of eyeballing CTR.
How it compares
Use for analytics methodology and KPI design—not as a substitute for a dedicated data-pipeline or BI MCP integration.
Common Questions / FAQ
Who is product-analyst for?
Solo and indie builders running product-led SaaS or APIs who own analytics decisions themselves and want agent-guided frameworks instead of generic growth blog posts.
When should I use product-analyst?
In Grow to interpret funnels and retention; during Validate to define success metrics before build; in Ship when tuning launch experiments; and in Build when scoping event instrumentation for new features.
Is product-analyst safe to install?
Review the Security Audits panel on this Prism page for the upstream package; the skill content is analytical guidance and does not inherently require network or secret access.
SKILL.md
READMESKILL.md - Product Analyst
## 1. Metrics Frameworks (metrics) **Impact:** CRITICAL **Description:** Core metrics frameworks like AARRR and HEART, KPI selection, metric hierarchies, and North Star definition. The foundation for all product analytics. ## 2. Funnel Analysis (funnel) **Impact:** CRITICAL **Description:** Conversion funnel design, drop-off analysis, bottleneck identification, and optimization strategies. Where most growth opportunities live. ## 3. Cohort & Retention (cohort) **Impact:** CRITICAL **Description:** Cohort analysis, retention curves, churn prediction, and lifecycle segmentation. The key to sustainable growth. ## 4. Feature Analytics (feature) **Impact:** HIGH **Description:** Feature adoption tracking, usage depth measurement, success criteria, and feature-level decision making. ## 5. Experimentation (experiment) **Impact:** HIGH **Description:** A/B testing design, hypothesis formulation, statistical rigor, and interpreting experiment results. ## 6. Data Instrumentation (instrumentation) **Impact:** HIGH **Description:** Event taxonomy, tracking implementation, data quality, and building reliable analytics foundations. ## 7. Dashboard Design (dashboard) **Impact:** MEDIUM-HIGH **Description:** Product dashboards, stakeholder reporting, self-serve analytics, and visualization best practices. --- title: Cohort Analysis and Retention impact: CRITICAL tags: cohort, retention, churn, lifecycle --- ## Cohort Analysis and Retention **Impact: CRITICAL** Cohort analysis separates the signal from the noise. It answers: "Are things getting better?" by comparing groups of users who share a common starting point. ### What is a Cohort? A cohort is a group of users who share a characteristic, typically their signup date (acquisition cohort) or first action date (behavioral cohort). ``` Week 1 Cohort: All users who signed up during Week 1 Week 2 Cohort: All users who signed up during Week 2 ... Compare: How does Week 5 cohort perform vs Week 1 cohort? ``` ### The Classic Retention Table ``` Week 0 Week 1 Week 2 Week 3 Week 4 Week 5 Cohort ────── ────── ────── ────── ────── ────── Jan 1 1,000 450 380 320 290 280 Jan 8 1,200 540 465 400 360 - Jan 15 1,100 520 440 385 - - Jan 22 1,300 610 530 - - - Jan 29 1,400 650 - - - - As Percentages: Week 0 Week 1 Week 2 Week 3 Week 4 Week 5 ────── ────── ────── ────── ────── ────── Jan 1 100% 45% 38% 32% 29% 28% Jan 8 100% 45% 39% 33% 30% - Jan 15 100% 47% 40% 35% - - Jan 22 100% 47% 41% - - - Jan 29 100% 46% - - - - ``` **Reading the table:** - **Rows:** Different cohorts (by signup week) - **Columns:** Time periods since signup - **Diagonal:** Same calendar week across cohorts - **Trend right:** How retention evolves over time for a cohort - **Trend down:** How retention improves/degrades for newer cohorts ### Types of Retention Metrics | Type | Definition | Use Case | |------|------------|----------| | **N-Day Retention** | % returning on exactly day N | Mobile apps, daily products | | **N-Day Rolling** | % still active after day N | Subscription products | | **Unbounded** | % returning on or after day N | Casual use products | | **Bracket/Range** | % active within a time window | Weekly/monthly products | **Examples:** ``` N-Day (D7): Active on exactly day 7 / Total users = 22% N-Day Rolling (D7): Active at least once days 7-14 / Total users = 35% Unbounded (D7): Active on day 7 OR any day after / Total users = 45% Bracket (Week 2): Active during days 8-14 / Total users = 32% ``` ### Retention Curves ``` 100% ┤ │ ● 80% ┤ ╲ │ ● 60% ┤ ╲ │ ●───●───●