Skill UI

This guide details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

This skill provides a comprehensive, multi-phase guide for authors to create a Specification Enhancement Proposal (SEP) adhering to formal governance standards. It guides the user through necessary prerequisites, consensus building, technical interviews (covering breaking changes, security, and prototypes), and structured research phases required to define new protocols or improvements for the Model Context Protocol (MCP).

This comprehensive guide provides a production-grade reference architecture for building robust Text-to-Speech (TTS) and voice applications using the ElevenLabs API. It covers detailed project structure, service layer design (including TTS orchestration, voice management, and caching), configuration handling, and best practices for streaming and multi-model orchestration, ensuring scalability and reliability in a commercial SaaS environment.

FastMCP CLI provides a unified command-line interface to interact with Model Context Protocol (MCP) servers. It allows users to discover available tools and invoke them programmatically using `fastmcp list` and `fastmcp call`. The CLI supports various server targets (HTTP URLs, local files, configs), complex argument types, and ensures structured output, making it essential for building automated, multi-tool workflows and system integrations.

This workflow is designed for developers building design-to-code pipelines. It connects to Figma via API to extract core design tokens, including colors, typography styles, and spacing values. It converts these raw design assets into structured code formats, such as CSS custom properties, JSON tokens, and Tailwind configurations, ensuring design consistency across development environments.

A comprehensive skill for building enterprise-grade integrations with Figma. It covers the full OAuth 2.0 authorization code flow, token refreshing, and secure handling of user access tokens. It provides methods for managing team structures, listing projects, accessing components/styles, and utilizing advanced, enterprise-only features like the Variables API.

This guide provides a comprehensive walk-through of making the first REST API calls to Figma. Users learn how to fetch core file metadata (name, version, pages) and traverse the complex document structure (nodes, frames, components). It's ideal for developers starting a new Figma integration, setting up API connectivity tests, or understanding the fundamental relationships between design elements within a Figma file.

This guide provides a comprehensive setup for local development workflows for Figma plugins and external REST API integrations. It enables developers to build, test, and iterate rapidly using hot reloading, mocked API fixtures, and structured project templates. Essential for creating design-to-code pipelines and advanced Figma tooling.

A comprehensive reference architecture for building production-grade integrations with the Figma REST API. This guide covers the full design-to-code pipeline, including design token extraction, scalable asset export (icons/images), and robust webhook event handling. Ideal for architects planning new Figma services, establishing project structure, or implementing complex design system workflows.

Generates deterministic, publication-quality vector diagrams (SVG) for architecture, workflows, and pipelines from structured JSON specifications (FigureSpec). This tool is designed for technical documentation and formal system modeling where absolute precision, reproducibility, and the ability to edit the output are critical. It ensures that the same input always yields the same, validated diagram.

This skill automatically converts structured data specifications (such as CSV, JSON schemas, SQL DDL, or Protobuf definitions) into functional, production-ready code. It generates complete models, validation logic, full CRUD API endpoints, and unit tests in multiple languages (Python, TypeScript, Go, etc.), significantly reducing boilerplate coding time and ensuring type safety.