Skill UI

This mode simulates a P9 Tech Lead role, specializing in strategic project management and complex task orchestration. It does not write code itself, but focuses on generating detailed, structured Task Prompts (six elements) and managing the entire workflow delivery from multiple specialized agents (P8 teams). Ideal for coordinating large, complex projects requiring strategic decomposition and coordination of three or more parallel agents.

This skill provides comprehensive automation capabilities for managing Asana workspaces. Users can automate complex tasks including creating, searching, and listing tasks; managing projects and sections; and listing teams and users. By utilizing the Rube MCP framework, it allows for the orchestration of multi-step workflows, significantly streamlining project management and collaboration processes.

A comprehensive guide for creating, updating, refactoring, and reviewing solutions built on the Microsoft Agent Framework. This skill provides unified best practices for both .NET and Python environments, serving as a modern successor to Semantic Kernel and AutoGen. It emphasizes adherence to the latest official documentation, async patterns, robust error handling, and structured agent/workflow orchestration for complex AI applications.

A robust, production-ready architecture pattern for integrating with multiple Adobe APIs, including Firefly Services, PDF Services, and I/O Events. This pattern enforces a layered structure (API -> Service -> Client -> Infrastructure), ensuring clean separation of concerns, centralized authentication, robust error handling boundaries, and scalable workflow orchestration for enterprise applications.

This skill provides the core mechanism for integrating Hex into external data pipelines. It enables triggering, monitoring, and managing Hex projects (e.g., via Airflow or Dagster) programmatically. Users can implement parameterized runs, execute multi-step ETL workflows sequentially, and handle status polling and error management, making it ideal for production data orchestration.

A comprehensive guide to setting up a robust local development loop for Hex API orchestration. This includes defining the project structure, creating a typed API client using TypeScript, and implementing mocked unit tests (e.g., using vitest) to ensure fast iteration and reliable testing of data pipelines and external service interactions.

This guide provides production-ready patterns for integrating with the Hex platform API using TypeScript and Python. It covers essential best practices like robust retry logic, typed client usage, pipeline orchestration, and Airflow integration, helping developers establish standardized, reliable coding standards for complex Hex workflows.

This guide details the recommended reference architecture and project structure for integrating with the Hex platform. It covers best practices for designing robust data pipelines, including components like orchestration layers (Airflow, Dagster), API clients, and standardized project layouts. Use this when starting new Hex integrations or reviewing complex data workflows.

This skill provides a comprehensive guide to building robust LangGraph 1.0 StateGraphs in Python. It covers defining typed TypedDict states with reducers, writing deterministic nodes, and defensively wiring conditional edges. It addresses common pitfalls such as silent graph termination, incorrect state updates using reducers, proper management of recursion limits, and handling evolving checkpoint schemas for reliable LLM workflow orchestration.

Master the creation of robust ELT (Extract, Load, Transform) data pipelines within Snowflake. This guide demonstrates advanced data orchestration using Snowflake Streams for Change Data Capture (CDC), Tasks for scheduled transformations and DAG management, and Dynamic Tables for declarative, auto-refreshing data models. Ideal for building sophisticated, scalable data warehousing solutions.

This tool allows users to execute complex, multi-agent workflows defined in YAML format directly within the AI environment. It functions as an orchestration engine, sequentially invoking various specialized roles (agents) without requiring manual API key configuration. Ideal for structured tasks requiring collaboration between multiple personas.

This workflow establishes a structured, multi-agent system to manage the entire software development lifecycle. It uses specialized roles (Producer, Developer, QA) to clarify scope, implement robust features, conduct rigorous testing, and maintain project context across sessions. Ideal for adopting new projects or coordinating complex, multi-step implementations.