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product-analytics

Business
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Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.

QUICK START

How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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Source SKILL.md: https://github.com/LeoYeAI/openclaw-master-skills/blob/HEAD/skills/product-analytics/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/product-analytics/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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
  1. Define stage-appropriate KPIs
  • Pre-PMF: activation, early retention, qualitative success
  • Growth: acquisition efficiency, expansion, conversion velocity
  • Mature: retention depth, revenue quality, operational efficiency
  1. Design dashboard layers
  • Executive layer: 5-7 directional metrics
  • Product health layer: acquisition, activation, retention, engagement
  • Feature layer: adoption, depth, repeat usage, outcome correlation
  1. 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
  1. 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:

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