Skill UI

This comprehensive automation toolkit manages core Bitbucket functionalities, including repositories, workspaces, pull requests, and issues. It enables programmatic control over development workflows—from creating and reviewing pull requests to listing repos, tracking issues, and managing workspace members. Essential for integrating CI/CD pipelines and streamlining the entire software development lifecycle.

FAF Expert is an advanced tool for creating standardized, persistent context for complex software projects. It uses the IANA-registered FAF format to transform codebases into AI-intelligent assets. Use it for multi-AI workflow management (Claude, Gemini, etc.), enterprise-grade deployment, and standardizing project context across development lifecycles.

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.

This tool searches the current repository and lists all pull requests assigned to the user. It provides comprehensive details for each PR, including its purpose and scope. The output is highly functional, highlighting pending review status, detailing any failed checks (with suggested fixes), and offering to automatically request a Copilot review to maintain continuous development flow. Ideal for daily workflow status checks.

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.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

This advanced agent acts as a highly constrained implementation expert, designed to execute complex, multi-step plans with absolute fidelity. It strictly adheres to the provided workflow, making only specified changes, updating the plan status in real-time, and running tests until completion. Ideal for disciplined software development, detailed project execution, or formal process adherence.

Update existing specification files for solutions based on new requirements or code changes. Ensures documentation is optimized for Generative AI consumption, machine-readable, and self-contained. Follows structured formatting standards including requirements, constraints, interfaces, and acceptance criteria to facilitate AI-driven development workflows.

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 developers to finalize a development branch after testing passes. It involves verifying tests, detecting the git environment, presenting integration options (merge, PR, keep), executing the chosen action, and cleaning up worktrees. It ensures safe integration and prevents accidental data loss during branch management.

A powerful utility for performing semantic searches across a large knowledge base of 105K+ skill cards. It allows users to find the most relevant skills based on natural language queries, supporting both Python API integration and Command Line Interface (CLI). This tool is zero-dependency and highly efficient for skill discovery and knowledge retrieval in development workflows.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.