Skill UI

This skill provides an interactive, guided onboarding tour for the Context-Matic MCP server. It addresses the challenge of general AI models relying on outdated or incorrect public code by acting as a live, version-aware grounding layer. The tour walks users through the server's core function, demonstrates key tools (like fetching APIs, searching models, and finding endpoints), and guides them through selecting and exploring available APIs for their specific project language.

Design, analyze, and refine Azure infrastructure through natural language prompts, generate interactive architecture diagrams locally with bundled scripts, and produce deployable Bicep templates to automate infrastructure-as-code workflows.

Interactive workflow that interrogates scope, deliverables, constraints, and success criteria while guiding users to refine task prompts; copies the final markdown with Joyride clipboard commands and never writes code.

Interactive training for the GitHub Copilot CLI with guided lessons, quizzes, scenario challenges, and a reference covering slash commands, shortcuts, agents, MCP, and configuration; say “cliexpert” to begin.

Provides Dataverse Python SDK snippets for installation, interactive authentication, CRUD, bulk operations, paging, and optional file uploads aligned with official guidance.

Interactive workflow that uses the joyride_request_human_input tool to probe scope, deliverables, and constraints before executing any task, ensuring thorough clarification and structured planning.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

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 interactive wizard guides you through every step required to establish a consistent and professional design system for your new project. It walks users through selecting app type, defining brand colors, applying visual concepts (like Stripe or Notion), choosing typography, and finally scaffolding the initial page structure, ensuring a solid foundation for development.

gget is a comprehensive command-line and Python package providing unified access to over 20 major bioinformatics databases. Users can perform deep analyses across genomics, proteomics, and disease associations. Key functions include gene information lookups, sequence alignment (BLAST), structural prediction (AlphaFold), single-cell analysis, and enrichment studies, supporting both interactive queries and automated pipelines.

Matplotlib is Python's foundational library for creating static, animated, and interactive visualizations. It provides fine-grained control over every plot element, supporting diverse plot types (line, scatter, heatmap, etc.) and complex multi-panel layouts. Users should leverage the object-oriented API for building robust, publication-quality figures and exporting them to formats like PNG, PDF, or SVG.

This tool automates the complete specification generation process across four core phases: Initialization, Requirements, Design, and Tasks. It supports both interactive (step-by-step approval) and automatic execution modes. After generating the full spec, it performs a final sanity review to ensure coherence and completeness, providing a ready-to-implement blueprint.