Skill UI

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.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

A comprehensive CI/CD pipeline for Quarkus applications, designed to verify code quality and deployment readiness. It covers all phases: build, static code analysis (SonarQube, Checkstyle), rigorous functional testing (Unit, Integration, API), security scanning (OWASP, dependency checks), native image compatibility testing, performance load testing (K6), and container image validation (Trivy, Grype).

A structured playbook for improving the time and space complexity (Big-O) of existing, slow code. This skill guides developers through systematic refactoring, focusing on identifying bottlenecks like nested loops or N+1 queries. It emphasizes applying transformations one at a time, maintaining test coverage, and measuring performance gains to ensure correct and efficient code.

Generates a complete, structured load and performance testing plan for any service. This skill guides the definition of test objectives, establishes measurable SLO thresholds (p99, error rate, throughput), and designs multi-scenario tests (Baseline, Stress, Spike, Soak). It is essential for teams needing to set clear pass/fail criteria, select appropriate tooling (k6, Locust), and integrate performance gates into CI/CD pipelines.

This skill generates a comprehensive and structured test strategy document. Given a feature specification, PRD, or system description, it systematically outlines the test scope, performs a detailed risk assessment, defines necessary test types (Unit, E2E, Performance, Security), and provides a prioritized, actionable test case outline to ensure robust quality assurance before release.

This skill converts vague speed requests into a structured optimization process. It establishes a baseline, defines correctness gates, and iteratively tests variants against metrics like latency or cost. Each variant is benchmarked and validated before promotion, ensuring reproducible improvements without breaking functionality. Ideal for performance tuning, recursive optimization, and selecting the best implementation through measured tests.

Automates comprehensive end-to-end Quality Assurance (QA) for deployed web applications. This skill performs multi-stage testing, including smoke tests, complex user interactions (forms, authentication), visual regression checks across breakpoints, performance monitoring (Core Web Vitals), and WCAG accessibility audits.

Bun is a high-performance, all-in-one JavaScript runtime and toolchain. It functions as a runtime, package manager, bundler, and test runner, offering significant speed improvements over traditional Node.js setups. It is ideal for modern, fast-paced development, new projects, and deployment environments like Vercel.

This skill analyzes historical benchmark run data stored in JSON files. It surfaces key performance drift metrics over time, including win rate, average latency, p99 latency, and LLM-baseline cost. It is crucial for detecting subtle performance degradations or regressions that standard smoke tests might miss, ensuring stability across releases or corpus expansions.

A comprehensive, multi-phase checklist for ensuring the quality, security, and deployment readiness of Django applications. It covers linting (ruff, mypy), unit testing (pytest), database migrations, security audits (bandit, pip-audit), and performance checks, making it an essential quality gate for CI/CD pipelines before merging or deploying to production.