Skill UI

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

A comprehensive guide for researchers preparing their academic artifacts (code, data, results) for the SIGMOD Availability & Reproducibility Initiative (ARI). This document details the criteria for achieving reproducibility badges (Available, Evaluated, Results Reproduced), focusing on coverage, ease of reproduction, flexibility, and portability. It provides best practices for structuring artifact repositories and ensuring hardware honesty for verifiable research claims.

This tool analyzes specified source code files or modules to automatically generate comprehensive and structured test suites. It identifies testable units (functions, classes, methods), adheres to project conventions (like AAA pattern), and ensures high coverage for critical scenarios, including happy paths, boundary edge cases, and error handling. It supports popular frameworks such as Jest, Vitest, and pytest.

A sophisticated, multi-agent system designed to perform deep, comprehensive code reviews on pull requests. It covers core elements like code quality, functionality, security vulnerabilities, and testing coverage. Advanced features include detecting scope drift, enforcing SOLID principles, identifying DRY violations, and providing anti-hallucination safeguards for maintain high software integrity.