Skill UI

A comprehensive guide and set of scripts for performing load testing and capacity planning for Anthropic's Claude API. This resource helps developers benchmark performance, anticipate traffic spikes, manage rate limits (RPM/ITPM/OTPM), and implement robust horizontal scaling strategies for mission-critical AI services using Python.

This comprehensive skill provides a framework for load testing and capacity planning of Figma API integrations. Using tools like k6, it helps benchmark API throughput, identify rate limit ceilings (e.g., leaky bucket behavior), and analyze latency under high load. It also outlines advanced scaling strategies—such as request coalescing and staggering—to ensure reliable, high-volume application integration.

A comprehensive guide and toolset for load testing HubSpot integrations and planning for high-volume traffic. This resource details HubSpot's specific API rate limits (per-second and daily) and provides k6 scripts and capacity calculators to ensure your application scales reliably before deployment in production.

This guide provides comprehensive methods for load testing and capacity planning specifically for Salesforce integrations. It walks users through calculating API limit budgets, running high-volume performance tests using tools like k6, and optimizing Bulk API throughput. Use this when scaling complex, high-volume, or mission-critical Salesforce processes.

This guide provides a comprehensive framework for load testing Shopify app integrations, specifically designed to respect strict API rate limits. It teaches users how to plan and benchmark system capacity for high-traffic events like Black Friday/Cyber Monday (BFCM) or flash sales using tools like k6. Users learn to optimize for burst events, analyze GraphQL cost, and configure infrastructure scaling for reliable e-commerce performance.

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.

This advanced orchestrator manages complex internal business workflows, covering process mapping, vendor management, capacity planning, and procurement optimization. It acts as a central router, classifying multi-signal inquiries and delegating tasks to specialized sub-skills (e.g., knowledge-ops, vendor-management). It follows a structured, depth-first workflow and returns actionable, digested reports anchored in documentation and next steps.

A sophisticated tool for Operations Leaders (CX, Support, BizOps) to model team capacity, headcount requirements, and utilization risk. Built on Erlang-C queueing theory and Little's Law, it provides capacity sizing (P50/P90/P99), utilization health checks, and a detailed 12-month quarterly hiring plan that accounts for ramp-up and attrition. Use it for annual planning and budget defense.

A comprehensive reference guide providing quantitative benchmarks and formulas for system architects. It covers user-to-RPS conversion, storage estimation, bandwidth calculation, known scale limits of major technologies (databases, queues, caches), and capacity planning for various user loads (1K DAU to 10M DAU). Essential for designing scalable, high-availability applications.

This skill generates a comprehensive capacity plan document for any service. It assists technical teams in proactively managing infrastructure by modeling traffic growth, forecasting resource utilization (CPU, memory, connections), projecting costs, and defining clear scaling strategies. Use this when assessing current resource headroom, planning for seasonal peaks, or defining a long-term infrastructure action roadmap.

This skill guides users through transforming a raw product backlog into a highly structured and actionable sprint plan. It generates a clear Sprint Goal, calibrates the backlog scope based on team velocity and capacity, provides a detailed capacity plan, identifies potential risks, and outputs a comprehensive meeting agenda. It ensures the resulting sprint is achievable, outcome-focused, and adheres to best practices (e.g., 80% commitment rate).

This tool analyzes historical sprint velocity data to generate a comprehensive engineering team health report. It moves beyond simple charts by diagnosing underlying delivery patterns, identifying risks like chronic over-commitment, and assessing story point calibration. The report provides actionable recommendations for improving capacity planning and team predictability.