metrics▌

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

Essential metrics framework for tracking startup performance across seed through Series A stages. \n \n Covers universal metrics (MRR, ARR, CAC, LTV, burn rate, runway) plus specialized frameworks for SaaS, marketplaces, consumer, and B2B models \n Includes formulas, calculation methods, and stage-specific benchmarks for evaluating unit economics and growth efficiency \n Provides stage-gated guidance: pre-seed focuses on product-market fit signals; seed emphasizes retention and baseline unit eco

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Measure and analyze code quality metrics to identify complexity, maintainability issues, and areas for improvement.

Query resource usage metrics for Railway services.