Skill UI

Generates realistic database seed data and scripts for development or testing, leveraging Faker libraries to respect schema constraints, maintain relationships, and allow configurable volumes for quick population of demo or test environments.

Generates end-to-end tests for web workflows using Playwright, Cypress, or Selenium, automating browser steps and assertions so QA teams can validate registration, login, shopping cart, and other multi-step flows across browsers and devices.

Generates realistic users, products, orders, or schema-driven data via the test-data-generator plugin for populating test databases, simulating user behavior, and creating fixtures for automated or API testing.

Automatically produce mocks, stubs, spies, or fakes for unit tests by analyzing dependencies and using frameworks like Jest or Sinon, so you can isolate modules quickly without hand-writing boilerplate doubles.

Aggregates test results from multiple frameworks, computes coverage/pass rate trends, and produces stakeholder-friendly HTML, PDF, or JSON reports for CI/CD test reports, coverage checks, failure analysis, or historical comparisons.

Automatically analyzes source files, detects or honors the requested testing framework, and produces comprehensive unit tests with mocks, edge cases, and assertions for Jest, pytest, JUnit, and other frameworks whenever users ask to generate tests.

This skill orchestrates a comprehensive, multi-stage Git development workflow, simulating the process from code commit to PR preparation. It leverages specialized AI agents (code reviewers, test automators, prompt engineers) to systematically enforce best practices. Key functions include code quality assessment, breaking change analysis, running unit/integration/E2E tests, and generating Conventional Commit messages, ensuring code readiness and high quality before merging.

This skill acts as a PR optimization expert, designed to help users create high-quality pull requests. It generates comprehensive descriptions, automates review processes, and ensures adherence to best practices regarding clarity, documentation, and test coverage. It streamlines the entire code review lifecycle.

A collection of production-ready GitHub Actions workflow patterns designed for automating the entire software development lifecycle (SDLC). Use these templates to streamline continuous integration (CI), build Docker images, deploy to Kubernetes clusters, perform security scans, and execute matrix testing across various tech stacks.

A comprehensive guide featuring best practices and patterns for building robust GitLab CI/CD pipelines. Covers multi-stage deployments, containerization (Docker), infrastructure as code (Terraform), security scanning, caching strategies, and advanced GitOps workflows for automated testing and deployment to Kubernetes.

Comprehensive Go guidance for browser automation and web scraping using Rod’s direct Chrome DevTools Protocol integration plus stealth anti-detection patterns, ideal for headless SPA testing, concurrent scraping, and request interception.

Automates Go test creation across unit, integration, and mocked scenarios while enforcing best practices for test automation workflows.