Metrics Frameworks
by menkesu
Defines right metrics using North Star framework, AARRR, and leading vs lagging indicators. Use when choosing metrics, instrumenting products, creating dashboards, or distinguishing vanity metrics from actionable ones.
Skill Details
Repository Files
1 file in this skill directory
name: metrics-frameworks description: Defines right metrics using North Star framework, AARRR, and leading vs lagging indicators. Use when choosing metrics, instrumenting products, creating dashboards, or distinguishing vanity metrics from actionable ones.
Metrics That Matter
When This Skill Activates
Claude uses this skill when:
- Choosing which metrics to track
- Instrumenting product analytics
- Creating dashboards
- Defining success criteria
Core Frameworks
1. North Star Metric
Definition:
The ONE metric that best captures the core value you deliver to customers
Good North Star:
- Measures value delivery (not vanity)
- Leading indicator of revenue
- Captures product vision
- Actionable by team
Examples:
- Airbnb: Nights booked
- Facebook: Daily active users
- Slack: Messages sent
- Amplitude: Weekly learning users (in Spotify)
2. AARRR Framework
Pirate Metrics:
- Acquisition: How users find you
- Activation: First value experience
- Retention: Users coming back
- Revenue: Monetization
- Referral: Viral growth
Action Templates
Template: Metrics Dashboard
# Metrics Dashboard: [Product/Feature]
## North Star Metric
**Metric:** [name]
**Current:** [value]
**Target:** [goal]
**Why this metric:** [captures core value]
## Supporting Metrics
### Acquisition
- Signups: [X per day]
- Channels: [breakdown]
- Cost per acquisition: [$X]
### Activation
- Activation rate: [X]%
- Time to first value: [X minutes]
### Retention
- Day 1: [X]%
- Day 7: [X]%
- Day 30: [X]%
### Revenue
- ARPU: [$X]
- LTV: [$X]
- Conversion rate: [X]%
### Referral
- K-factor: [X]
- Referral rate: [X]%
## Leading vs Lagging
**Leading (predict future):**
- [Metric that predicts outcome]
**Lagging (measure past):**
- [Metric that measures result]
Quick Reference
📊 Metrics Checklist
Choose Metrics:
- North Star defined
- AARRR covered
- Leading indicators identified
- Vanity metrics avoided
Implement:
- Tracking instrumented
- Dashboard created
- Goals set
- Review cadence established
Key Quotes
Amplitude:
"The best metrics measure value delivery, not just activity."
Sean Ellis:
"If you can't measure it, you can't improve it."
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.