Skill UI

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.

Detects operations in cryptographic code that leak secrets via timing side channels, guiding developers through multi-language analysis and CLI flags for warnings, filtering, architecture, and optimization levels.

Generates type-safe client SDKs in multiple languages (TypeScript, Python, Go, Java) from OpenAPI specs, complete with auth handling, retries, pagination helpers, doc comments, and per-language tests for automated publishing workflows.

Automatically assists backend developers with gRPC service generation, offering best-practice patterns, multi-language scaffolds for Node.js, Python, Go, and validating configs for databases, caching, and messaging to stay production-ready.

This powerful tool helps translate natural language requirements into robust, normalized relational database schemas. It can generate comprehensive schema designs, complete with relationships, constraints, and advanced architectural concerns like multi-tenancy, soft deletes, and audit trails. Furthermore, it automates the generation of database migrations (supporting Prisma, TypeORM, Alembic, etc.), corresponding type definitions (TypeScript/Python), seed data, and Entity-Relationship Diagrams (ERD) using Mermaid.

Guide for writing YAML-based Semgrep rules to detect app-specific vulnerabilities, enforce coding standards, and plug into CI/CD for continuous SAST assurance across multiple languages.

A powerful operational companion for database developers. This skill assists with the daily lifecycle of database interactions, including translating natural language requirements into optimized SQL queries, generating database migrations, performing comprehensive schema introspection across multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), and identifying performance bottlenecks through detailed query optimization and index recommendations. It also supports ORM patterns like Prisma and SQLAlchemy.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

This skill automates the comprehensive localization and translation of an entire MkDocs documentation suite. It translates all Markdown files from source English folders into a specified target language, preserving the exact folder structure and Markdown formatting. Additionally, it updates include references and modifies the core mkdocs.yml configuration file to properly support the new locale, ensuring a seamless multilingual documentation setup.

This guide details the process of implementing internationalization (i18n) in a Next.js application using the next-intl library. It provides step-by-step instructions on adding new languages, covering translation updates, configuring routing, modifying middleware, and updating UI components to ensure complete multi-language support.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.