Skill UI

A reflection-based container for implementing dependency injection in Golang. It provides advanced features like Provide/Invoke, named values, value groups, optional dependencies, and scopes, allowing developers to structure and manage complex application object graphs at startup. Ideal for setting up application frameworks and services in Go.

This skill generates comprehensive, tailored instruction files for AI coding assistants like GitHub Copilot and Claude. It supports multiple strategies (flat or nested) and is specifically designed for managing complex monorepos by creating area-scoped instructions using `applyTo` globs. Use it to standardize AI tooling usage across large codebases.

This router serves as the initial guide for comprehensive security assessments on new targets or unknown attack surfaces. It helps users map the scope, discover hidden assets, perform detailed technology fingerprinting, build endpoint inventories, and strategically plan the optimal testing path before diving into specific vulnerability checks (e.g., API, Auth, Injection).

This skill guides developers on integrating CommunityToolkit.Mvvm ViewModels with Microsoft's Dependency Injection system. It covers setting up the composition root using the .NET Generic Host, managing service lifetimes (Singleton, Transient, Scoped), implementing constructor injection, and correctly registering messaging services (IMessenger) for robust, cross-platform XAML applications (WPF, MAUI, Uno, Avalonia).

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.

This skill converts vague coding requests into a single, actionable, and detailed project plan. It guides the user through defining scope, establishing concrete, ordered action items (discovery, changes, tests, rollout), and identifying potential risks or open questions, ensuring a robust development workflow.

This meta-skill automates the process of handling a GitHub issue by fetching context, delegating the fix to a sub-agent, summarizing the resolution, and finally opening a pull request (PR). It is best suited for small, well-scoped issues with clear acceptance criteria. This pipeline simulates a complete development workflow.

This tool traces the entire downstream dependency chain from a core semantic model to all connected reports and assets across multiple Fabric workspaces. Use it for crucial impact analysis before modifications, auditing model usage, or mapping cross-workspace data dependencies. It helps data stakeholders understand the full scope of influence a model has on the reporting layer.

This skill generates formal, audit-ready Standard Operating Procedures (SOPs) suitable for regulated industries, ISO certification, or internal process standardization. It structures the document with defined sections including Purpose, Scope, Definitions, Responsibilities, detailed Step-by-step Procedures, Quality Checks, and Non-Conformance handling, ensuring compliance and clarity for operational scaling.

This skill generates a comprehensive and structured test strategy document. Given a feature specification, PRD, or system description, it systematically outlines the test scope, performs a detailed risk assessment, defines necessary test types (Unit, E2E, Performance, Security), and provides a prioritized, actionable test case outline to ensure robust quality assurance before release.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

A comprehensive tool and methodology for identifying and preventing dependency confusion attacks across major package ecosystems (npm, PyPI, Maven). It detects instances where internal, private package names are leaked and subsequently published publicly by attackers. The skill provides both detection (enumerating claimable internal names) and prevention strategies (enforcing registry pinning and scope restrictions) to secure the software supply chain.