Skill UI

Guides product managers through turning an Epic or feature request into a detailed PRD—with goal, personas, stories, requirements, and acceptance criteria ready for engineers.

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.

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.

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.

Automates the creation of GitHub issues by parsing an implementation plan, verifying existing issues, and generating one issue per phase using feature or chore templates with structured titles and requirements.

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.

Creates a machine-readable specification template tailored for Generative AI solutions, covering purpose, requirements, constraints, interfaces, dependencies, and validation so implementation stays unambiguous.

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 meta-skill template designed for developers to scaffold, structure, and define specialized Agent Skills for AI platforms like GitHub Copilot. It guides users through mandatory requirements, including folder naming conventions, YAML frontmatter best practices (name, description), and structuring the skill body with detailed workflows and documentation sections. Use this when building complex, specialized AI capabilities.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

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.