Skill UI

This tool transforms natural language requirements into high-quality, structured Mermaid diagrams. It supports over 20 diagram types, including Flowcharts, Sequence Diagrams, Class Diagrams, ER Diagrams, and Gantt Charts. The process includes parsing requirements, generating raw Mermaid code, and verifying syntax using industry-standard tools, ensuring accurate and professional visualizations for technical documentation.

A comprehensive skill for managing, provisioning, and destroying GPU instances on vast.ai. It analyzes your deep learning task to determine precise hardware requirements (VRAM, GPU count, storage) and searches for cost-optimized, high-reliability GPU offers. The skill handles the entire lifecycle from initial analysis to experiment monitoring, ensuring you only rent what you need at the best possible value.

This skill generates highly structured, long-form blog content (800-3000 words) optimized for both SEO ranking and AEO citation requirements. It automatically includes essential elements like a TL;DR summary block, defining sentences, comparison tables, and 5 distinct FAQs. This structure ensures the content is easily digestible, highly authoritative, and optimized for extraction by modern AI engines and search algorithms. Ideal for building topical authority and content clusters.

This comprehensive guide details how to properly integrate and configure Firebase within Flutter applications. It covers initial setup using `flutterfire configure`, essential initialization in `main.dart`, handling platform-specific requirements, and advanced techniques for managing multiple app flavors (dev, staging, prod).

A systematic methodology for initiating projects, features, or major changes. It mandates creating a detailed, structured specification (spec) before writing any code, ensuring all assumptions are surfaced and vague requirements are translated into measurable success criteria. This process involves four validated phases: Specify, Plan, Tasks, and Implement, guaranteeing alignment between the human team and the development process.

This tool analyzes specification files to extract all defined requirements and cross-references them with the current codebase implementation status. It systematically checks for unimplemented features, searches existing GitHub issues to prevent duplicates, and automatically generates detailed issues using a standardized feature request template. It is essential for robust product management, ensuring every defined requirement is tracked and scheduled for development.

A comprehensive guide to creating production-grade Product Requirements Documents (PRDs). This skill helps bridge the gap between vague business concepts and concrete technical specifications for modern software systems and AI features. It enforces a structured workflow, ensuring all necessary components—from user stories and success KPIs to technical architecture and risk analysis—are defined.

Generates a structured Epic architecture specification from a Product Requirements Document, guiding senior architects to capture high-level approaches, layered Mermeid diagrams, features, technical enablers, stack choices, and value estimates for TypeScript/Next.js monorepo projects.

Generates comprehensive Epic-level PRDs for large SaaS initiatives, translating high-level requests into structured goals, journeys, requirements, and value summaries saved for architecture planning.

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.