Skill UI

A comprehensive guide for authors revising technical papers for top-tier conferences like ISSTA. It details the necessary structure, emphasizing strict scope definition, establishing clear evaluation contracts (subjects, baselines, metrics), pairing every claim with concrete evidence, and adhering to specialized review discipline.

A comprehensive guide and checklist for ensuring that figures and tables in medical manuscripts comply with the rigorous reporting standards of JAMA. It covers mandatory elements such as CONSORT flow diagrams, reporting effect sizes with 95% CIs, structuring baseline characteristics, and avoiding statistical misrepresentation (e.g., using p-values instead of CIs).

A comprehensive guide for advanced quantitative research, focusing on demonstrating the validity of econometric methods. It outlines best practices for combining rigorous Monte Carlo simulations (stress-testing DGPs, baselines) with substantive real-world data applications, essential for publication in high-impact economics and statistics journals.

A comprehensive guide for designing and presenting rigorous experiments for top-tier computer science journals. It covers best practices for ensuring reproducibility, matching evidence to claims, selecting appropriate baselines, performing statistical significance testing, and handling modern challenges like LLM contamination and system scalability measurements. Essential for passing double-blind peer review.

A guide for integrating behavioral theory into rigorous empirical studies. It outlines the necessary steps to move beyond descriptive findings of irrationality by constructing a statistically identifiable behavioral model (e.g., incorporating loss aversion parameters) and testing it against rational baselines using controlled, incentive-compatible experimental designs. Essential for submitting papers requiring model explanation of behavioral biases.

A comprehensive guide for designing and presenting rigorous experiments for submissions to the Journal of Software (JOS). It guides users through setting clear Research Questions (RQ), selecting real-world datasets and baselines, applying advanced statistical testing (effect size, significance), conducting ablation studies, and addressing threats to validity (internal, external, conceptual). This ensures the evidence strongly supports the claims, meeting top-tier academic standards.

Provides detailed guidelines for creating high-standard clinical figures and tables, modeled after leading medical journals (e.g., The Lancet). It covers essential display items including Table 1 (baseline characteristics), Kaplan-Meier survival curves (with numbers-at-risk), Forest plots for meta-analyses, CONSORT/PRISMA flow diagrams, and global maps. The guide emphasizes the rigorous reporting of 95% confidence intervals (CIs) and proper statistical methods to ensure scientific accuracy.

Performs a comprehensive security audit of Model Context Protocol (MCP) implementations, including servers, clients, and tools. This skill reviews code against critical security baselines (MCP-01 to MCP-05), OWASP MCP Top 10, and common Remote Code Execution (RCE) vectors (e.g., command injection, deserialization, SSRF). The output provides a detailed report with file and line references, ensuring compliance and identifying vulnerabilities before release. Use this when auditing network-exposed services or client security posture.

This guide provides a rigorous framework for evaluating computer architecture designs. It covers critical steps including selecting the appropriate simulation instrument (from analytical models to silicon measurements), constructing robust baselines, performing ablation studies, and reporting comprehensive metrics like geomeans and full overhead accounting to ensure scientific validity.

This skill guides researchers on how to structurally position a Marketing Science manuscript within its academic literature. It requires defining the model's baseline (modeling lineage) and the specific business problem it addresses (substantive stream). Crucially, it teaches how to frame the contribution as a concrete field gain rather than merely pointing out an existing knowledge gap.

Provides a comprehensive, time-sensitive playbook for authors responding to reviews (rebuttals) at MLSys. It details how to triage objections, prioritize critical missing measurements, address systems objections regarding workload representativeness and baseline tuning, and structure anonymous, data-driven replies within a compressed rebuttal window.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.