Skill UI

This skill provides comprehensive, programmatic automation capabilities for the Telegram platform via Rube MCP. Users can manage virtually every aspect of Telegram interaction, including sending various media types (text, photos, documents), retrieving detailed chat information, listing members, editing and deleting messages, and setting custom bot commands. It is essential for building sophisticated, automated bot workflows and integrations.

This comprehensive tool suite provides programmatic automation for Todoist task management. Users can manage the entire task lifecycle, including creating, updating, completing, and deleting tasks. It also supports structured organization by managing projects and sections, and filtering tasks using advanced workflows. Ideal for integrating task management into automated business processes or applications.

Automatically update implementation plan files to reflect new requirements, ensuring structured, machine-readable phases with explicit tasks and validation criteria for AI agents to execute deterministically.

This skill helps design the complete user journey or 'flow' before building individual screens. It applies established UX principles (like progressive disclosure and hub-and-spoke models) to define entry points, exit paths, and the overall navigation architecture. Use it for complex tasks like onboarding, checkout, or multi-step features to ensure a coherent, logical, and highly intuitive user experience.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

This comprehensive guide serves as a technical reference for migrating applications from the Universal Windows Platform (UWP) framework to the modern WinUI 3 / Windows App SDK. It details crucial API changes, including namespace updates, window management (CoreWindow to AppWindow), threading models (CoreDispatcher to DispatcherQueue), and best practices for dialogs and background tasks. Essential for developers updating legacy UWP codebases.

Automates Wrike project management via Rube MCP’s Composio toolkit, covering secure connection setup, task/folder creation, blueprint launches, progress tracking, and workspace/member control while always fetching schema info first.

This skill guides the systematic execution of a detailed, written development plan. It mandates a critical review phase to identify dependencies, vague requirements, and environment assumptions before starting. The process involves tackling tasks sequentially, performing rigorous verification (testing/checking), committing changes after each milestone, and systematically handling common exceptions like test failures or dependency gaps. It ensures a disciplined, traceable, and reportable development cycle.

This technique is used when multiple failure points (e.g., different test files or subsystems) are discovered, and these failures are independent of each other. Instead of debugging them sequentially, you dispatch separate, focused AI agents to work on each problem domain concurrently. This maximizes efficiency by allowing parallel investigation, dramatically reducing the time spent on large-scale debugging tasks.

This guide details the best practices for utilizing a code review sub-agent. It should be used after completing tasks, implementing major features, or before merging code into main branches. The process ensures that work results are validated against requirements, allowing reviewers to focus purely on the output rather than the development history, thus catching issues early.

This skill outlines the principle of mandatory, evidence-based verification before declaring any work (such as a feature, bug fix, or task) complete or successful. It mandates that assertions of success must be backed by repeatable evidence, such as passing test outputs, clean build logs, or successful execution reports. It is critical for PR reviews, task closure, and maintaining high engineering rigor.

This skill is designed to facilitate communication and collaboration between different AI agents. Use it when you need to consult an expert agent, request a plan, seek a review, or continue a context-aware conversation with a specialized agent. It handles both real-time foreground chat and asynchronous background task submission, ensuring robust multi-agent workflows.