Skill UI

This skill enhances AI responses by simulating the inner monologue and self-correction process of a human being. It moves beyond standard helpful assistance by incorporating three stages: Self (raw instinct), Mirror (critical reflection), and Conscious Response (the polished, human-like output). Ideal for emotional support, personal advice, or writing that requires authentic emotional depth and a distinct point of view, rather than purely factual or structured data.

Generates implementation-ready JSON-LD schema for FAQPage, Article, Product, HowTo, and other types, validating required fields against Google rich result rules while outputting single script blocks plus guidance so teams can deploy structured data with confidence.

Analyzes Excel and CSV uploads with DuckDB, inspecting schemas, running SQL, summarizing statistics, and exporting results so users can explore structured data efficiently.

Provides a well-structured skill template with metadata for `lark-cli`, helping developers verify formatting and readiness before deployment.

This advanced prompt acts as a structured guide for generating comprehensive, enterprise-grade technical implementation plans. It forces the detailed breakdown of a feature based on a PRD, covering system architecture (Mermaid diagrams), database schema design, API specifications, frontend component hierarchy, and deployment strategies, all while adhering to a monorepo structure. Ideal for large-scale SaaS development.

Learn how to add Adaptive Card response templates to MCP-based API plugins. This guide details static, dynamic, and combined templates, enabling developers to enhance data presentation and ensure a visually rich, structured user experience within Microsoft 365 Copilot.

This skill provides comprehensive command-line interface (CLI) functionality for managing the entire lifecycle of experiments within the Arize platform. Users can list, retrieve metadata, create new experiments from various data files (CSV, JSON, Parquet), and export detailed run results for deep analysis. It supports linking experiments to specific dataset versions, making it essential for structured ML model evaluation and performance comparison workflows.

This skill analyzes Azure resource groups in depth, identifying all resources, their configurations, and complex interdependencies. It maps relationships—including network connections, data flows, and managed identities—to generate comprehensive, detailed Mermaid architecture diagrams. Use this when you need a clear, structured visualization and full documentation of an Azure environment's topology.

Guides an AI pair to document application needs, access patterns, and scaling concerns while producing Azure Cosmos DB NoSQL requirements and data model artifacts using established best practices in a structured workflow.

Generates an AI-ready Architectural Decision Record template with precise sections, coded bullet conventions, front-matter metadata, and sequential storage under /docs/adr to keep decision documentation structured for both machines and people.

This tool automates the entire workflow of creating a GitHub Pull Request (PR) directly from a structured specification file. It analyzes the template, extracts requirements, checks for existing PRs, creates the draft, updates the body and title with specification details, and finally transitions the PR state to 'Ready for Review'. This significantly streamlines the feature request submission process, ensuring all necessary documentation and metadata are included.

This skill analyzes an entire .NET solution (containing multiple projects) to map out a systematic migration path from Oracle to PostgreSQL. It scans for common Oracle indicators (NuGet packages, connection strings, code usage) and classifies every project into categories like MIGRATE, SKIP, or ALREADY_MIGRATED. The output is a structured, actionable Master Migration Plan, helping development teams manage large-scale database transitions efficiently.