metrics▌

Comprehensive SaaS metrics analysis covering MRR, ARR, Churn, LTV, CAC, cohort analysis, and investor reporting. Essential for SaaS founders, finance teams, and investors.

Fast lookup table for 30+ SaaS finance metrics, formulas, benchmarks, and red flags. \n \n Covers four metric families: Revenue & Growth, Unit Economics, Capital Efficiency, and Efficiency Ratios, each with formula, benchmark, and red flag \n Includes four decision frameworks for feature investment, channel scaling, pricing changes, and business health assessment by stage \n Organized for speed: scan the reference table, check red flags by category, apply the relevant framework to your deci

Evaluate SaaS unit economics and capital efficiency to determine if your business model can scale sustainably. \n \n Covers 15+ metrics across unit economics (CAC, LTV, payback, gross margin), capital efficiency (burn rate, runway, Rule of 40), and GTM performance (magic number, operating leverage) \n Includes segment-specific analysis templates and quality checks for each metric to catch common pitfalls like high LTV:CAC ratios masking poor payback periods \n Designed for PMs to make scaling de

Calculate and interpret core SaaS revenue, retention, and growth metrics to diagnose business momentum and product-market fit. \n \n Covers 10+ essential metrics: MRR/ARR, ARPU/ARPA, churn rate, NRR, expansion revenue, and Quick Ratio, with formulas and quality checks for each \n Includes cohort analysis and revenue mix frameworks to reveal hidden trends that blended metrics obscure (e.g., cohort degradation, concentration risk) \n Provides benchmarks for each metric (e.g., <2% monthly chur

Framework for defining a single, customer-centric metric that aligns teams and drives product decisions. \n \n Measure value delivered to users, not revenue or internal activity; use proxy metrics like messages sent or nights booked that correlate with long-term outcomes \n Ensure simplicity and universal understanding across the company; avoid composite scores and technical jargon that obscure meaning \n Verify actionability by confirming teams can influence the metric through their work, and a

Query CPU, memory, network, and disk usage metrics for Railway services. \n \n Supports nine metric types including CPU usage, memory, network traffic (RX/TX), and disk usage across deployments and instances \n Query metrics for a single service or all services in an environment using optional groupBy parameters (by deployment, instance, region, or service) \n Requires environmentId from railway status --json ; serviceId is optional to retrieve metrics across all services \n Time-based queries w

Startup Metrics Framework \n Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A. \n Use this skill when \n \n Working on startup metrics framework tasks or workflows \n Needing guidance, best practices, or checklists for startup metrics framework \n \n Do not use this skill when \n \n The task is unrelated to startup metrics framework \n You need a different domain or tool outside this scope \n \n

query-metrics

Retrieve and display GitHub Copilot usage metrics for organizations and enterprises. \n \n Supports both organization-level and enterprise-level metrics queries, with optional filtering by specific date (YYYY-MM-DD format) \n Provides aggregated metrics (total active users, acceptance rates, suggestions, chat interactions) and per-user breakdowns via separate commands \n Requires GitHub Enterprise Cloud, appropriate token permissions ( manage_billing:copilot or read:enterprise scope), and the \"

Portfolio risk measurement with VaR, CVaR, Sharpe, Sortino, and drawdown analysis. \n \n Covers 15+ risk metrics across volatility, tail risk, drawdown, and risk-adjusted return categories with parametric, historical, and Cornish-Fisher VaR methods \n Includes rolling window analysis, portfolio-level calculations with marginal risk contribution and risk parity optimization, and stress testing against historical crises or hypothetical shocks \n Supports Monte Carlo simulation with elevated volati