Skill UI

This skill generates complete gRPC service definitions, client/server stubs, and robust implementations directly from Protocol Buffer (.proto) files. It supports all streaming patterns (unary, server, client, bidirectional) and includes advanced features like authentication interceptors, health checks, and automatic REST-to-gRPC gateway generation, making it ideal for building high-performance, enterprise-grade microservices.

Automatically produce mocks, stubs, spies, or fakes for unit tests by analyzing dependencies and using frameworks like Jest or Sinon, so you can isolate modules quickly without hand-writing boilerplate doubles.

A comprehensive toolkit for ensuring code quality in modern React and Next.js applications. It automatically generates unit test stubs using Jest and React Testing Library, scaffolds Playwright E2E tests for entire application routes, and analyzes coverage reports to identify crucial testing gaps. Ideal for developers looking to establish, improve, and maintain robust, enterprise-grade test suites.

Automates stub creator tasks within test automation, offering step-by-step guidance, industry best practices, and production-ready code/configs for mocking, unit, and integration tests while validating outputs against common standards whenever stub creator patterns are mentioned.

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.

A comprehensive guide on using the Mockito package for unit testing in Dart and Flutter. Learn best practices for distinguishing between real, fake, and mock objects, along with detailed syntax for generating mocks, stubbing methods, verifying interactions, and capturing arguments to ensure robust, isolated testing.

A comprehensive guide on using the Mocktail package for advanced mocking in Dart and Flutter. It covers best practices for distinguishing between Mocks, Fakes, and Real objects, along with detailed instructions on stubbing methods, verifying interactions, registering fallback values, and using argument matchers. Essential for writing reliable unit tests.

This guide details how to scaffold complete, structured exercise directory setups, including mandatory sections for problems, reference solutions, and conceptual explainers. It outlines precise naming conventions, required files (like readme.md and main.ts), and a robust workflow that ensures the setup passes linting checks. It is ideal for creating new course modules, generating exercise stubs, or structuring large-scale educational curricula.

This stub experiment generator is designed for the meta-paper-write demo. It takes a topic phrase and deterministically produces a 20-row CSV file containing baseline and generated results. This ensures stable and reproducible results for demonstration purposes, but should not be used for real scientific data.

This tool reads a CSV containing three columns (x, y_baseline, y_ours). It uses matplotlib to generate a two-line plot visualizing the results and saves the final output as a PDF file. It is specifically designed for visualizing performance comparisons in scientific papers.

This utility reads structured JSON outputs from multi-search-engine APIs (e.g., Google Scholar, arXiv) provided via standard input. It converts these normalized records into a clean, minimal BibTeX format, suitable for academic papers. It intelligently preserves crucial metadata like DOI, authors, and years while automatically handling data sanitization, deduplication of DOIs, and protecting complex fields like corporate names.

An internal tool designed to validate candidate meta-skills (SKILL.md) before they are registered in the system. It performs comprehensive linting checks, including structural validation (G1: parsing, reference checks, XML escaping) and a scheduler dry-run (G2) using stub executors, ensuring the skill is deterministic and functional. Output diagnostics in JSON format.