Skill UI

Client library for Azure AI Translator service enabling real-time text translation, transliteration, language detection, dictionary lookup, and sentence boundary detection. Supports multiple languages, custom endpoints, and Azure Entra ID authentication.

This skill combines automated static analysis and AI tools like GitHub Copilot and SonarQube to identify bugs, vulnerabilities, and performance issues in code. It supports multi-language reviews, integrates with CI/CD pipelines, and provides actionable recommendations for security, architecture, and maintainability across various development workflows.

Analyze cryptographic code to identify operations leaking secret data via timing variations. Detects timing attacks and side-channel vulnerabilities in implementations like encryption, signing, and key derivation across multiple programming languages.

This skill covers strategies for optimizing Large Language Model context windows to prevent context rot. It includes techniques like summarization, trimming, routing, and prioritization to manage token budgets effectively. Developers can use these methods to build multi-turn conversation systems where context length is a constraint, leveraging tools like tiktoken and LangChain for implementation.

This skill automates the creation of isolated, pre-configured development containers (devcontainers). It scans the project structure to detect supported languages (Python, Node.js, Rust, Go) and intelligently generates the necessary configuration files, VS Code extensions, and setup scripts. It ensures all required tools are installed in a sandboxed environment, making it ideal for starting new multi-language projects or needing consistent, reproducible development workspaces.

A comprehensive guide covering Internationalization (i18n) and Localization (L10n) best practices. It details how to build applications that can support multiple languages, regions, and text directions (RTL). Covers implementation patterns for React, Next.js, and Python, along with essential guidelines for handling date/number formats and managing translation keys.

Lambda is a compact, machine-native communication protocol specifically designed for agent-to-agent (A2A) messaging. It functions as a shared, unambiguous vocabulary built from 'atoms' (2-character codes), completely bypassing the need for human natural language interpretation. It is ideal for orchestrators, task delegation, and logging structured coordination signals (like heartbeats or state changes) in multi-agent systems. Crucially, Lambda is intended only for machine communication, not for user-facing dialogue.

This skill guides users through the entire lifecycle of developing intelligent AI assistants, chatbots, and conversational interfaces. It specializes in establishing robust natural language understanding, managing multi-turn context, and defining best practices for creating seamless, production-ready AI applications with real-world value.

This skill generates standardized, production-ready `mise.toml` configurations. It helps developers standardize runtime versions across mixed-language projects (e.g., Node.js, Python, Go, etc.) and CI/CD pipelines, eliminating the need for multiple legacy version managers like asdf or nvm. Ideal for ensuring reproducible, consistent development environments.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

This skill covers the setup and configuration of Static Application Security Testing (SAST) tools like Semgrep, SonarQube, and CodeQL. It guides users on creating custom security rules, integrating scans into CI/CD pipelines, and managing quality gates. The process includes optimizing scan performance, reducing false positives, and ensuring compliance with security policies across multiple programming languages.