Skill UI

This comprehensive guide outlines a multi-stage verification pipeline for Quarkus applications. It covers build artifacts, static analysis (Checkstyle, SonarQube, PMD), comprehensive testing (Unit, Integration, API), security scanning (OWASP, dependency checks), native compilation, and performance testing (K6). Use this before PRs or deployments to ensure code quality and stability.

This skill executes comprehensive historical backtesting using the high-performance neural-trader engine. It supports features like walk-forward validation and captures key financial metrics (Sharpe, Drawdown). Critically, it signs the resulting artifact using Ed25519 cryptography, providing non-repudiable tamper evidence required for promoting research models to live trading systems.

Design a comprehensive and systematic framework for optimizing ad creative performance. This tool guides users through defining testable variables (headlines, CTAs, imagery), designing A/B and multivariate tests, calculating required sample sizes, and establishing optimal iteration cadences across major advertising platforms (Meta, Google, etc.) for continuous marketing improvement.

This guide provides a comprehensive framework for designing rigorous, falsifiable system evaluations for academic research papers. It details best practices across four critical evidence layers: end-to-end performance, mechanism decomposition, operational cost, and boundary testing. Learn how to ensure workload realism, maintain baseline fairness, structure your experiment matrix, and avoid common pitfalls to meet top-tier systems research standards.

This comprehensive guide helps authors assess if their electrochemistry manuscript (covering batteries, fuel cells, supercapacitors, etc.) is suitable for the Journal of Power Sources. It instructs users on how to reframe purely material-focused advances into compelling device-and-electrochemistry stories, emphasizing cell-level performance, degradation analysis, and rigorous testing standards.

Provides a comprehensive methodology for evaluating mobile computing systems. It mandates rigorous, real-device testing, focusing on instrumenting energy consumption, thermal behavior, latency distribution (tails), and memory limits. This guide ensures academic submissions are credible, repeatable, and compare performance against robust, tuned baselines.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.