Skill UI

This skill guides developers on integrating Firebase AI (Gemini) into Flutter applications. It covers essential topics including initial setup and configuration, generating text and managing multi-turn chat responses, implementing real-time streaming output, handling multimodal prompts (text and images), and ensuring robust error handling, security, and adherence to data privacy best practices.

Copilot Spaces allow users to consolidate and manage project-specific knowledge, including documentation, code snippets, architectural standards, and operational instructions. By loading a Space, Copilot grounds its responses not just on general knowledge, but on the curated, authoritative context of the entire project, enabling accurate Q&A, code generation, and workflow execution based on team standards. Ideal for onboarding, large-scale projects, and maintaining consistency.

The GitHub Copilot SDK empowers developers to embed advanced, agentic AI workflows directly into their proprietary applications. It provides a programmatic interface to Copilot's production-tested agent runtime, allowing you to define complex agent behaviors, manage tool invocation, process file edits, and implement real-time streaming responses. Ideal for building custom, highly intelligent tools and automating complex tasks across multiple languages (Python, TypeScript, Go, .NET).

This is the foundational plumbing layer connecting AI agents to the Power Automate platform via FlowStudio MCP. It provides essential capabilities such as secure authentication setup, robust tool discovery (`list_skills`, `tool_search`), and handling complex, oversized API responses. It serves as the prerequisite layer for specialized workflow skills (e.g., building, debugging, monitoring).

Defines TypeSpec API plugin CRUD operations for Microsoft 365 Copilot, wiring GET/POST/PATCH/DELETE routes with parameters, adaptive-card responses, and confirmation prompts to illustrate a complete REST experience.

A comprehensive guide for software developers on how to respond to code review feedback with technical rigor. It emphasizes questioning, verification, and evidence-based rebuttal, moving beyond superficial agreement. Learn how to handle ambiguous feedback, apply the YAGNI principle, and maintain technical integrity in collaborative development environments.

This guide provides a comprehensive setup for a fast and reliable local development loop for applications powered by the Anthropic Claude API. It covers environment management, implementing hot reloading, tracking API usage costs, and utilizing mock responses to ensure a smooth, rapid iteration cycle for AI developers.

This comprehensive guide details advanced techniques for optimizing Bright Data API scraping performance. It covers implementing connection pooling, robust response caching layers (LRU Cache), and managing concurrent requests with backpressure control. Use this when facing slow API responses or requiring high-throughput, scalable data extraction.

This guide outlines advanced strategies to optimize latency and throughput when integrating Anthropic's Claude API. Learn critical techniques such as streaming responses, prompt caching, model selection (Haiku, Sonnet, Opus), and request parallelization, ensuring your applications provide a low-latency, high-performance user experience.

This guide details setting up a robust local development environment for Grammarly API integrations. It covers establishing the project structure, implementing mocked API responses, and utilizing vitest for comprehensive unit and integration testing. Ideal for developers who require a fast iteration cycle or need to configure reliable API test workflows.

This comprehensive guide provides advanced strategies for optimizing interactions with the Klaviyo API. It covers implementing response caching (LRU Cache), efficient cursor-based pagination, request batching using DataLoader, and controlling API concurrency. Use this when dealing with slow API responses, large datasets, or high-throughput integration requirements.

A set of production-ready wrappers and patterns for interacting with the CAST AI REST API. This package provides robust client implementations in TypeScript and Python, handling critical features such as typed responses, singleton management, automatic retry logic with backoff, and managing cloud resources (e.g., Kubernetes clusters, node optimization). Use it when building reusable, reliable clients for cost optimization and infrastructure management.