Skill UI

Automates CodeQL database builds, suite selection, and taint-aware scans to surface security and quality issues across supported languages, handling output directories, SARIF results, and extensions in one workflow.

This skill acts as a specialized expert for transitioning codebases across various frameworks, languages, versions, and platforms. It generates detailed migration plans, automated migration scripts, comprehensive risk assessments, and rollback strategies. Use it when upgrading technology stacks or moving code between disparate systems to ensure minimal disruption and maintain core functionality.

This skill provides a comprehensive set of cross-language patterns and best practices for achieving memory-safe systems programming. It covers fundamental concepts such as RAII, ownership models, smart pointers, and robust resource management. Use this guide when writing low-level or systems code, managing critical resources (files, sockets, memory), or debugging complex memory issues to prevent use-after-free errors and memory leaks.

Guides the user through configuring and tuning database connection pools across languages, from selecting pool sizes to setting timeouts and monitoring usage to boost application performance.

A comprehensive specialist that combines automated static analysis (SonarQube, CodeQL, Semgrep) with advanced AI capabilities. This tool performs multi-layered code reviews assessing security vulnerabilities, performance bottlenecks, architectural coherence, and maintainability across various programming languages, integrating seamlessly into CI/CD pipelines.

Guides teams through setting up and tuning SAST tools like Semgrep, SonarQube, and CodeQL, integrating scans into CI/CD, creating custom rules, and managing false positives to maintain secure codebases across languages.

This comprehensive guide assists developers in adopting Test-Driven Development (TDD) practices. It covers generating unit tests for various languages (Python, JS, Java), analyzing test coverage gaps across multiple frameworks (Jest, Pytest, JUnit), and guiding the iterative Red-Green-Refactor workflow. Use this guide when generating stubs, improving test coverage, or adhering to spec-first development methodologies.

Comprehensive TDD workflow guidance covering the Red/Green/Refactor rhythm, Arrange-Act-Assert test structure, Jest/Vitest, pytest, and Go patterns, coverage rules, mocking advice, and anti-pattern warnings to keep teams disciplined across languages.

This skill acts as an expert test automation specialist, capable of analyzing source code across multiple languages (Python, JavaScript, TypeScript, etc.). It generates comprehensive, maintainable unit test suites using best practices, maximizing code coverage, and specifically targeting edge cases and exception handling. Use it to ensure high code quality and structural consistency.

Scans cryptographic implementations to flag operations whose timing decisions leak secrets, offering language-specific guides, architecture/optimization options, and output modes so teams can enforce constant-time practices across reviews and CI.

Ports existing Semgrep rules into new language variants, guiding applicability analysis, test-first creation, and validation so teams can expand rule coverage across polyglot codebases with confidence.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.