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+---
+name: product-analytics
+description: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
+---
+
+# Product Analytics
+
+Define, track, and interpret product metrics across discovery, growth, and mature product stages.
+
+## When To Use
+
+Use this skill for:
+- Metric framework selection (AARRR, North Star, HEART)
+- KPI definition by product stage (pre-PMF, growth, mature)
+- Dashboard design and metric hierarchy
+- Cohort and retention analysis
+- Feature adoption and funnel interpretation
+
+## Workflow
+
+1. Select metric framework
+- AARRR for growth loops and funnel visibility
+- North Star for cross-functional strategic alignment
+- HEART for UX quality and user experience measurement
+
+2. Define stage-appropriate KPIs
+- Pre-PMF: activation, early retention, qualitative success
+- Growth: acquisition efficiency, expansion, conversion velocity
+- Mature: retention depth, revenue quality, operational efficiency
+
+3. Design dashboard layers
+- Executive layer: 5-7 directional metrics
+- Product health layer: acquisition, activation, retention, engagement
+- Feature layer: adoption, depth, repeat usage, outcome correlation
+
+4. Run cohort + retention analysis
+- Segment by signup cohort or feature exposure cohort
+- Compare retention curves, not single-point snapshots
+- Identify inflection points around onboarding and first value moment
+
+5. Interpret and act
+- Connect metric movement to product changes and release timeline
+- Distinguish signal from noise using period-over-period context
+- Propose one clear product action per major metric risk/opportunity
+
+## KPI Guidance By Stage
+
+### Pre-PMF
+- Activation rate
+- Week-1 retention
+- Time-to-first-value
+- Problem-solution fit interview score
+
+### Growth
+- Funnel conversion by stage
+- Monthly retained users
+- Feature adoption among new cohorts
+- Expansion / upsell proxy metrics
+
+### Mature
+- Net revenue retention aligned product metrics
+- Power-user share and depth of use
+- Churn risk indicators by segment
+- Reliability and support-deflection product metrics
+
+## Dashboard Design Principles
+
+- Show trends, not isolated point estimates.
+- Keep one owner per KPI.
+- Pair each KPI with target, threshold, and decision rule.
+- Use cohort and segment filters by default.
+- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).
+
+See:
+- `references/metrics-frameworks.md`
+- `references/dashboard-templates.md`
+
+## Cohort Analysis Method
+
+1. Define cohort anchor event (signup, activation, first purchase).
+2. Define retained behavior (active day, key action, repeat session).
+3. Build retention matrix by cohort week/month and age period.
+4. Compare curve shape across cohorts.
+5. Flag early drop points and investigate journey friction.
+
+## Retention Curve Interpretation
+
+- Sharp early drop, low plateau: onboarding mismatch or weak initial value.
+- Moderate drop, stable plateau: healthy core audience with predictable churn.
+- Flattening at low level: product used occasionally, revisit value metric.
+- Improving newer cohorts: onboarding or positioning improvements are working.
+
+## Tooling
+
+### `scripts/metrics_calculator.py`
+
+CLI utility for:
+- Retention rate calculations by cohort age
+- Cohort table generation
+- Basic funnel conversion analysis
+
+Examples:
+```bash
+python3 scripts/metrics_calculator.py retention events.csv
+python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
+python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
+```
diff --git a/product-team/product-analytics/references/dashboard-templates.md b/product-team/product-analytics/references/dashboard-templates.md
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+# Dashboard Templates
+
+## 1. Executive Dashboard Template
+
+Purpose: quick company-level product signal for leadership.
+
+Sections:
+1. North Star trend (current, target, trailing 12 periods)
+2. Growth summary (new users/accounts, activation)
+3. Retention summary (short-term + medium-term cohorts)
+4. Revenue-linked product indicators
+5. Risks and actions
+
+Suggested KPI block:
+
+| KPI | Current | Target | Delta | Owner | Action |
+|---|---:|---:|---:|---|---|
+| North Star |  |  |  |  |  |
+| Activation Rate |  |  |  |  |  |
+| W8 Retention |  |  |  |  |  |
+| Paid Conversion |  |  |  |  |  |
+
+## 2. Product Health Dashboard Template
+
+Purpose: monitor full user journey and detect bottlenecks.
+
+Sections:
+1. Acquisition funnel by channel/segment
+2. Activation funnel with drop-off points
+3. Cohort retention matrix + curve chart
+4. Feature adoption distribution
+5. Reliability metrics tied to user outcomes
+
+Recommended views:
+- Weekly cohort retention heatmap
+- Funnel stage conversion waterfall
+- Segment comparison (SMB vs enterprise)
+- New vs returning user behavior split
+
+## 3. Feature Adoption Dashboard Template
+
+Purpose: evaluate feature launch quality and ongoing usage.
+
+Sections:
+1. Exposure and eligibility count
+2. First-use adoption rate
+3. Repeat usage rate (2nd, 3rd, nth use)
+4. Time-to-adoption from signup/activation
+5. Impact on primary outcomes (retention, conversion)
+
+Adoption KPI examples:
+
+| Metric | Definition |
+|---|---|
+| First-use adoption | Users who used feature at least once / eligible users |
+| Repeat adoption | Users with 2+ uses / users with first use |
+| Sustained adoption | Users with usage in 3 of last 4 weeks |
+| Time to adoption | Median days from eligibility to first use |
+
+## Dashboard Design Rules
+
+- Keep each dashboard to one decision horizon (weekly ops vs quarterly strategy).
+- Always annotate major product releases on charts.
+- Add threshold bands for risk detection.
+- Show metric definitions next to charts.
+- Include a short "what changed" narrative block.
diff --git a/product-team/product-analytics/references/metrics-frameworks.md b/product-team/product-analytics/references/metrics-frameworks.md
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+# Metrics Frameworks
+
+## AARRR (Pirate Metrics)
+
+AARRR breaks the product journey into five stages.
+
+1. Acquisition
+- How users discover the product
+- Example metrics: signups, CAC, channel conversion
+
+2. Activation
+- First meaningful value moment
+- Example metrics: activation rate, time-to-first-value
+
+3. Retention
+- Ongoing user return behavior
+- Example metrics: D7/W4 retention, rolling retained users
+
+4. Revenue
+- Monetization and value capture
+- Example metrics: conversion to paid, ARPU, expansion revenue
+
+5. Referral
+- Organic growth from existing users
+- Example metrics: referral rate, invite conversion, K-factor
+
+## North Star Metric Framework
+
+North Star = metric capturing long-term customer value delivered.
+
+### North Star Criteria
+- Reflects real user value
+- Sensitive to product improvements
+- Predictive of sustainable growth
+- Understandable across functions
+
+### Example North Star Metrics
+- Collaboration SaaS: weekly active teams
+- Marketplace: successful transactions per active buyer
+- Content product: hours of qualified consumption
+
+### Input Metrics
+Track levers that influence the North Star:
+- Acquisition quality
+- Activation quality
+- Engagement depth
+- Retention durability
+
+## HEART Framework
+
+HEART is a UX-oriented framework from Google.
+
+- Happiness: satisfaction, NPS, perceived quality
+- Engagement: interaction depth/frequency
+- Adoption: first-time use of features/products
+- Retention: return behavior over time
+- Task Success: completion rate, error rate, time on task
+
+### HEART + Goals-Signals-Metrics
+1. Goals: what UX outcome you want
+2. Signals: observed behavior indicating movement
+3. Metrics: measurable indicator for each signal
+
+## Framework Selection Guide
+
+| Situation | Recommended Framework |
+|---|---|
+| Early growth and funnel bottlenecks | AARRR |
+| Company-wide strategic alignment | North Star |
+| UX and product quality optimization | HEART |
+| Mixed maturity org | North Star + AARRR operational layers |
+
+## Example: B2B SaaS Product
+
+- North Star: weekly active accounts completing core workflow
+- AARRR operational metrics:
+  - Acquisition: qualified signups
+  - Activation: % accounts completing setup in 7 days
+  - Retention: W8 retained accounts
+  - Revenue: paid conversion and expansion rate
+  - Referral: invited teammate activation rate
+- HEART for onboarding redesign:
+  - Task Success: onboarding completion rate
+  - Happiness: onboarding CSAT
diff --git a/product-team/product-analytics/scripts/metrics_calculator.py b/product-team/product-analytics/scripts/metrics_calculator.py
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+#!/usr/bin/env python3
+"""Product metrics calculator: retention, cohort matrix, and funnel conversion."""
+
+import argparse
+import csv
+import datetime as dt
+from collections import defaultdict
+
+
+def parse_date(value: str) -> dt.date:
+    return dt.date.fromisoformat(value.strip()[:10])
+
+
+def load_csv(path: str):
+    with open(path, "r", encoding="utf-8", newline="") as handle:
+        return list(csv.DictReader(handle))
+
+
+def retention(args: argparse.Namespace) -> int:
+    rows = load_csv(args.input)
+    cohorts = {}
+    activity = defaultdict(set)
+
+    for row in rows:
+        user = row[args.user_column].strip()
+        cohort_date = parse_date(row[args.cohort_column])
+        activity_date = parse_date(row[args.activity_column])
+        cohorts[user] = min(cohorts.get(user, cohort_date), cohort_date)
+        delta = (activity_date - cohorts[user]).days
+        if delta >= 0:
+            activity[delta].add(user)
+
+    base_users = len(cohorts)
+    if base_users == 0:
+        print("No users found.")
+        return 1
+
+    print("Retention by period")
+    print("period,active_users,retention_rate")
+    max_period = args.max_period
+    for period in range(0, max_period + 1):
+        users = len(activity.get(period, set()))
+        rate = users / base_users
+        print(f"{period},{users},{rate:.4f}")
+    return 0
+
+
+def cohort(args: argparse.Namespace) -> int:
+    rows = load_csv(args.input)
+    cohorts = {}
+    activity = defaultdict(set)
+
+    for row in rows:
+        user = row[args.user_column].strip()
+        cohort_date = parse_date(row[args.cohort_column])
+        activity_date = parse_date(row[args.activity_column])
+
+        if args.cohort_grain == "month":
+            cohort_key = cohort_date.strftime("%Y-%m")
+        else:
+            cohort_key = f"{cohort_date.isocalendar().year}-W{cohort_date.isocalendar().week:02d}"
+
+        cohorts.setdefault(user, cohort_key)
+        age = (activity_date - cohort_date).days
+        if age >= 0:
+            activity[(cohort_key, age)].add(user)
+
+    cohort_sizes = defaultdict(int)
+    for cohort_key in cohorts.values():
+        cohort_sizes[cohort_key] += 1
+
+    cohort_keys = sorted(cohort_sizes.keys())
+    print("cohort,age_days,active_users,cohort_size,retention_rate")
+    for cohort_key in cohort_keys:
+        size = cohort_sizes[cohort_key]
+        for age in range(0, args.max_period + 1):
+            active_users = len(activity.get((cohort_key, age), set()))
+            rate = (active_users / size) if size else 0
+            print(f"{cohort_key},{age},{active_users},{size},{rate:.4f}")
+    return 0
+
+
+def funnel(args: argparse.Namespace) -> int:
+    rows = load_csv(args.input)
+    stages = [item.strip() for item in args.stages.split(",") if item.strip()]
+    if not stages:
+        print("No stages provided.")
+        return 1
+
+    stage_users = {stage: set() for stage in stages}
+    for row in rows:
+        user = row[args.user_column].strip()
+        stage = row[args.stage_column].strip()
+        if stage in stage_users:
+            stage_users[stage].add(user)
+
+    print("stage,users,conversion_from_previous,conversion_from_first")
+    previous_count = None
+    first_count = None
+    for stage in stages:
+        count = len(stage_users[stage])
+        if first_count is None:
+            first_count = count
+        conv_prev = (count / previous_count) if previous_count else 1.0
+        conv_first = (count / first_count) if first_count else 0
+        print(f"{stage},{count},{conv_prev:.4f},{conv_first:.4f}")
+        previous_count = count
+    return 0
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Calculate retention, cohort, and funnel metrics from CSV data."
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    common = {
+        "help": "CSV input path",
+    }
+
+    retention_parser = subparsers.add_parser("retention", help="Calculate retention by day.")
+    retention_parser.add_argument("input", **common)
+    retention_parser.add_argument("--user-column", default="user_id")
+    retention_parser.add_argument("--cohort-column", default="cohort_date")
+    retention_parser.add_argument("--activity-column", default="activity_date")
+    retention_parser.add_argument("--max-period", type=int, default=30)
+    retention_parser.set_defaults(func=retention)
+
+    cohort_parser = subparsers.add_parser("cohort", help="Build cohort retention matrix rows.")
+    cohort_parser.add_argument("input", **common)
+    cohort_parser.add_argument("--user-column", default="user_id")
+    cohort_parser.add_argument("--cohort-column", default="cohort_date")
+    cohort_parser.add_argument("--activity-column", default="activity_date")
+    cohort_parser.add_argument("--cohort-grain", choices=["week", "month"], default="week")
+    cohort_parser.add_argument("--max-period", type=int, default=30)
+    cohort_parser.set_defaults(func=cohort)
+
+    funnel_parser = subparsers.add_parser("funnel", help="Calculate funnel conversion by stage.")
+    funnel_parser.add_argument("input", **common)
+    funnel_parser.add_argument("--user-column", default="user_id")
+    funnel_parser.add_argument("--stage-column", default="stage")
+    funnel_parser.add_argument("--stages", required=True)
+    funnel_parser.set_defaults(func=funnel)
+
+    return parser
+
+
+def main() -> int:
+    parser = build_parser()
+    args = parser.parse_args()
+    return args.func(args)
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())