Skill UI

This guide provides comprehensive best practices for creating optimized, secure, and efficient multi-stage Dockerfiles. Learn how to separate build environments (builder stage) from minimal runtime environments, significantly reducing image size and attack surface. Topics covered include layer caching optimization, security hardening (non-root users), and using minimal base images for reproducible builds.

This tool searches the current GitHub repository and retrieves all issues explicitly assigned to the user. It goes beyond simple listing by analyzing key issue metadata—such as age, comment volume, and current status (open/closed)—to proactively suggest which issues require immediate attention or focus. It is indispensable for contributors, project managers, or developers who need a quick, prioritized overview of their assigned workload.

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.

A comprehensive guide to configuring complex nodes in workflow automation platforms like n8n. Learn best practices for understanding operation-specific field requirements, managing property dependencies, and utilizing progressive disclosure techniques (Standard vs. Full detail) to ensure accurate and efficient workflow setup.

This guide provides proven architectural patterns for building complex, reliable, and scalable workflows using n8n. It covers core patterns like Webhook processing, HTTP API integration, database synchronization, AI agent design, and scheduled tasks. Learn how to select the right pattern and utilize advanced components for robust automation.

Translates every approval request, error, and technical output from Copilot CLI into clear, jargon-free English with color-coded risk cues, definitions, and step-by-step commentary for non-technical stakeholders so they always understand what happens and why.

Connects to Google NotebookLM to query your uploaded documentation via Gemini’s source-grounded answers, automating authentication, notebook management, and question workflows through the provided run.py wrapper.

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 skill provides comprehensive automation for managing OneDrive file lifecycles. Users can perform advanced operations including searching across metadata and content, uploading and downloading files, organizing folders, creating share links, and granularly managing permissions for specific users. It utilizes a structured workflow approach via Rube MCP, making it ideal for automating business processes involving cloud document storage and collaboration.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Guides designers through using the Penpot MCP tools to craft professional UI/UX for web, mobile, and desktop apps, covering workflows, design systems, accessibility checks, and setup/troubleshooting steps for the Penpot MCP server.

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.