Skill UI

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.

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.

This skill provides structured procedures for managing NuGet packages in .NET projects and solutions. It strictly enforces the use of the `dotnet` CLI for adding, removing, and updating dependencies, prioritizing project integrity. It guides users through a mandatory workflow for version updates, including version verification and running `dotnet restore`, ensuring a stable and consistent development environment.

Continuous file-based planning system for multi-step AI agents. Maintains task_plan.md, findings.md, and progress.md files on disk, injecting project-specific planning context into lifecycle hooks. Auto-restore reads project planning files only. Can explicitly check local agent session metadata with session-catchup.py --metadata, or issue limited nonce-framed excerpts with --replay. Optional controlled mode requests continuation only when supported by host, never executes declared Markdown commands. Does not include a data upload path over the network. Used for research or work requiring 5+...

This skill enables AI agents to maintain persistent planning context using Markdown files (task_plan.md, findings.md, progress.md). It supports lifecycle hooks for context injection and session recovery, ensuring continuity in multi-step workflows. Ideal for complex research or tasks requiring more than 5 tool calls without network uploads.

Generates detailed prompts to document end-to-end application workflows by auto-detecting architectures, stacks, entry points, services, data access, error handling, and tests across .NET, Java/Spring, React, microservices, and other tech stacks.

Scaffolds an empty xUnit integration test project targeting Oracle in .NET solutions, including a transaction-rollback base class and seed data manager. Use in Phase 3 before writing Oracle baseline tests for Oracle-to-PostgreSQL migration.

A comprehensive tool that analyzes complex codebases to generate detailed, structured architectural blueprints. It automatically detects and documents the entire technology stack, including primary languages (.NET, Java, Python, etc.), dependencies, versions, licensing, and implementation patterns. The output provides actionable documentation covering coding conventions, usage examples (API, data access), and visual architecture maps, ensuring consistency across guided software development projects.

A comprehensive CLI for managing the entire lifecycle of Windows applications. It handles initialization, building, packaging into MSIX, signing, debugging, and advanced UI automation via Microsoft UI Automation. Supports multiple modern frameworks including .NET, C++, Electron, Rust, and Flutter, streamlining the deployment process to the Microsoft Store.

A comprehensive guide providing best practices for Git workflows, covering adaptation strategies for major Chinese platforms (Gitee, Coding.net, etc.), branch naming conventions, standardized commit messaging (Chinese Conventional Commits), and CI/CD integration across various domestic environments. Helps streamline development processes for Chinese teams.

This skill guides the management of CI/CD pipelines within KubeSphere DevOps. It utilizes Kubernetes Custom Resources (CRs) to define, trigger, and monitor complex build workflows, seamlessly integrating with Jenkins concepts in a cloud-native manner. Use it for automating build, test, and deployment processes for applications running on Kubernetes.

Expert technique for narrowing down Asian drama candidates by leveraging streaming platform and broadcast channel information. Use this when the query mentions services like Netflix, Viki, or Amazon Prime, combined with genre and year details. This method significantly increases search precision, especially when combining platform data with temporal or thematic keywords.