Skill UI

This comprehensive guide outlines essential best practices and structural checks for developing robust Salesforce Apex code. It covers critical areas such as preventing governor limit exceptions (e.g., bulk safety), enforcing security (CRUD/FLS), preventing SOQL injection, adhering to sharing models, and implementing modern Apex idioms. It also mandates rigorous testing strategies (Positive, Negative, Bulk) and proper trigger architecture.

Leverage AI-powered exploratory testing to thoroughly audit web applications and verify functionality. This skill simulates user interaction, detecting complex bugs, validating critical flows (like checkout or login), and checking accessibility compliance. Use it proactively after development to ensure quality before deployment.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

This skill provides expert-level control over a live Chrome browser. It enables full web automation (clicking, form filling, navigation), deep debugging (analyzing console logs, network requests), visual inspection (screenshots, snapshots), and detailed performance profiling. Ideal for QA engineers, web scraping, and front-end development testing.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Guides on structuring NUnit test projects, naming fixtures, arranging tests, and applying standard plus data-driven attributes along with assertions and mocking tactics to keep suites clear, independent, and repeatable.

A comprehensive guide covering best practices for writing effective unit tests using the TUnit framework for .NET. Topics include structuring tests using the Arrange-Act-Assert (AAA) pattern, implementing data-driven tests via attributes, utilizing fluent assertion syntax, managing complex test lifecycles (Class/Assembly level hooks), and migrating concepts from xUnit to TUnit. Learn how to write robust, isolated, and advanced tests.

Provides guidance on organizing and writing focused XUnit unit tests, covering project setup, fact/theory patterns, assertions, mocking, and data-driven testing for reliable .NET test suites.

Provides an end-to-end kit for building Microsoft 365 Copilot declarative agents across basic, enterprise, and validation workflows, with TypeSpec definitions, JSON schema validation, and Agents Toolkit integration for planning, testing, deployment, and optimization.

Guides teams through testing messaging and word choice to find defensible market positioning, using a phased Crawl-Walk-Run rollout plus word-change experiments to accelerate enterprise deals.

Phoenix Evals enables developers to build and run evaluators for AI and LLM applications. It supports both code-based and LLM-based evaluation methods, including RAG systems. Users can create datasets, run experiments, validate evaluator accuracy, and implement production guardrails. Available in Python and TypeScript, it helps ensure model performance and reliability through systematic testing and human validation.