Skill UI

This tool analyzes and refactors code to detect and correct anemic domain models. It enforces rich domain modeling patterns (Entities, Value Objects, Aggregates, Domain Events), improving encapsulation and business logic ownership. Use it when validating, reviewing, or refactoring core domain logic to ensure adherence to Domain-Driven Design (DDD) principles.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

Generate data analysis reports by exploring database tables, running multidimensional SQL queries, performing deep attribution analysis, and outputting dynamic HTML visual reports with ECharts for business, trend, and KPI scenarios.

This skill performs comprehensive, feature-level integration validation after all associated development tasks are implemented. It goes beyond checking individual tasks to ensure cross-task consistency, full test suite passage, and complete specification coverage. It validates data flow, architectural adherence, and performs critical security scans for residual issues (e.g., TODOs, hardcoded secrets), providing a holistic GO/NO-GO assessment for deployment. Use this skill as a final quality gate to verify that the completed feature set respects the designed boundaries and dependencies end-...

A comprehensive guide for connecting resource-constrained microcontrollers (like Arduino) to Azure IoT Hub and IoT Edge. It provides structured patterns for secure provisioning, handling intermittent networks (store-and-forward), robust telemetry transmission (MQTT over TLS), and implementing local edge decision-making for industrial and field deployments.

This skill guides users through building comprehensive Smart City and IoT solutions on the Azure platform. It covers the entire solution lifecycle, from initial high-level requirements and defining city domains (e.g., traffic, energy) to designing robust, scalable architectures. Outputs include detailed reference architectures, security checklists, cost strategies, and phased implementation roadmaps for enterprise deployment.

This tool assists engineers in transforming vague work logs into structured, evidence-backed impact statements for professional documentation. It uniquely mines Copilot CLI session logs, git commits, and GitHub PRs to reconstruct forgotten work. It enforces a mandatory 'Action → Result → Evidence' impact contract, making it indispensable for preparing performance reviews, promotion packets, and self-assessments.

A comprehensive guide to using `go.uber.org/dig` for implementing dependency injection in Go. It covers core concepts like `Provide`/`Invoke`, parameter object embedding (`dig.In`), result objects (`dig.Out`), named values, value groups, and making providers as interfaces. Ideal for setting up application graphs at startup in Go projects.

Uber FX is a robust application framework designed for building long-running Go services. It combines a reflection-based dependency injection (DI) container (built on dig) with essential features like lifecycle hooks (OnStart, OnStop), modular composition, and signal-aware execution. It ensures proper service initialization, graceful shutdown, and structured wiring for production-grade Go applications.

This skill guides the process of transforming private, repetitive operational workflows developed in Hermes into sanitized, reusable artifacts suitable for the public ECC layer. It ensures that sensitive data, local file paths, personal credentials, and private state are stripped out, allowing the core pattern to be safely published for broader use.

This skill guides the end-to-end development, implementation, and validation of production-ready Python modules for Azure IoT Edge. It covers critical aspects including telemetry processing, protocol adaptation (Modbus, OPC-UA), local inference, and ensuring robust, secure edge-to-cloud messaging under variable network conditions.

This skill automates the process of making any codebase ready for advanced AI assistance. It analyzes your repository structure, review patterns, and stack details to generate essential configuration files, including AGENTS.md, copilot-instructions.md, CI workflows, and issue templates. Use this when setting up a new project or integrating advanced AI tooling into your existing development workflow.