Skill UI

Guides an AI pair to document application needs, access patterns, and scaling concerns while producing Azure Cosmos DB NoSQL requirements and data model artifacts using established best practices in a structured workflow.

Generates a concise, AI-optimized specification describing what an existing GitHub Actions workflow does, its triggers, requirements, inputs/outputs, and governance so teams can understand and maintain it without implementation details.

This tool automates the entire workflow of creating a GitHub Pull Request (PR) directly from a structured specification file. It analyzes the template, extracts requirements, checks for existing PRs, creates the draft, updates the body and title with specification details, and finally transitions the PR state to 'Ready for Review'. This significantly streamlines the feature request submission process, ensuring all necessary documentation and metadata are included.

Generate structured specification files optimized for Generative AI consumption. Define requirements, constraints, and interfaces clearly using standardized Markdown templates. Ensure machine-readability and self-containment for effective AI processing and implementation.

Guides architects through Dataverse SDK use cases by analyzing requirements, designing data models, selecting integration patterns, and producing production-ready Python code with best practices.

A comprehensive guide to configuring complex nodes in workflow automation platforms like n8n. Learn best practices for understanding operation-specific field requirements, managing property dependencies, and utilizing progressive disclosure techniques (Standard vs. Full detail) to ensure accurate and efficient workflow setup.

A comprehensive, multi-phase quality engineering audit tool designed to test codebases beyond standard unit tests. It first explores the codebase's domain and architecture to derive behavioral requirements, then executes a rigorous sequence including spec-traced functional tests, three-pass code reviews, and a multi-model specification audit (Council of Three). It is specifically engineered to find the critical 35% of real-world defects that structural code review alone might miss. Works with any programming language.

Defines Salesforce Lightning Web Component, Aura, and Visualforce quality/security standards covering SLDS 2 styling, accessibility (WCAG 2.1 AA), data access patterns, component communication rules, XSS/CSRF protections, view state management, and Jest testing requirements for guided UI development and reviews.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

Automatically update implementation plan files to reflect new requirements, ensuring structured, machine-readable phases with explicit tasks and validation criteria for AI agents to execute deterministically.

Update existing specification files for solutions based on new requirements or code changes. Ensures documentation is optimized for Generative AI consumption, machine-readable, and self-contained. Follows structured formatting standards including requirements, constraints, interfaces, and acceptance criteria to facilitate AI-driven development workflows.

Implements verification gates as explicit phase checkpoints, producing acceptance checklists, CI gate configs, and manual approval rules so teams catch defects before phase transitions, covering requirements, design, implementation, integration, and deployment.