Skill UI

This skill provides a comprehensive suite for executing load testing, stress testing, and performance benchmarking. Use it to simulate peak traffic, identify system bottlenecks, and verify compliance with Service Level Agreements (SLAs). It supports industry-standard tools like k6, Artillery, Locust, and JMeter for robust performance analysis.

kube-bench is an open-source tool that validates the compliance and security posture of Kubernetes clusters. It rigorously checks control-plane components, worker nodes, and cluster policies against the comprehensive CIS Kubernetes Benchmark. It reports findings (PASS/FAIL/WARN) and includes specific remediation guidance, making it essential for compliance auditing and hardening efforts.

This comprehensive guide provides rigorous standards for advanced materials manuscripts. It mandates that structure–property claims must be validated through multiple, complementary characterization techniques (triangulation). It also details best practices for benchmarking against the state-of-the-art, ensuring reproducibility, and correctly partitioning core information between the main text and supplementary materials.

A comprehensive guide for authors submitting high-impact materials research. It details how to select expert referees (covering synthesis, device, and application domains) and provides actionable strategies to pre-empt common reviewer objections—such as claims of incremental advance, insufficient characterization, or weak benchmarking—thereby strengthening the manuscript's overall rigor and maximizing publication success.

A comprehensive guide for authors targeting high-impact chemical engineering venues. It helps authors re-frame their work from simple material synthesis to demonstrating an applied advance with clear mechanistic insight. Focus areas include catalysis, separation, functional materials, and performance benchmarking under realistic process conditions.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.