Skill UI

This guide outlines the necessary structure and high level of mathematical rigor required for theoretical manuscripts in Mathematical Finance. It covers techniques for establishing precise assumptions (e.g., filtrations, integrability), stating theorems cleanly, and constructing self-contained proofs for results in stochastic control, no-arbitrage pricing, and PDEs. It emphasizes methodological novelty over empirical identification.

This guide teaches required skills for positioning mathematical finance research papers. It instructs authors on how to structure the literature review to clearly demonstrate methodological novelty by comparing their theorem precisely against existing results in stochastic analysis, pricing, and control theory. Focus must be on theorem-level contribution, not merely topic surveys.

A detailed guide for researchers tackling advanced financial-mathematics problems. It outlines the stringent criteria for suitability in top-tier journals, emphasizing methodological novelty, mathematical rigor, and deep theoretical contribution (e.g., stochastic analysis, structural theorems) over routine data computation. Use this to refine and scope your research proposal.

A highly detailed writing style guide for academic manuscripts in Mathematical Finance. It instructs authors on structuring formal theorems, maintaining rigorous notation discipline, separating financial intuition from technical proofs, and ensuring consistency across stochastic analysis contexts. Ideal for improving the clarity and academic rigor of complex quantitative research papers.

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 assists researchers in selecting and rigorously defending the appropriate methodology for Management Science (INFORMS) manuscripts. It covers both analytical modeling approaches (e.g., optimization, stochastic processes, game theory) and empirical designs (e.g., quasi-experiments, field trials). It ensures the chosen design not only matches the research question but also meets strict departmental standards and generates decision-relevant managerial insights.

This guide provides essential best practices for structuring technical academic papers, especially when facing strict page limits (like a 12-page cap). It teaches authors how to properly divide content into the core body (main results, mechanism) and supplementary appendices (derivations, full protocols). The goal is to make the contribution self-contained while maximizing clarity and impact for reviewers.

This comprehensive guide instructs authors on creating mandatory, high-impact scientific communication artifacts for top-tier journals. It covers writing concise, mechanistic 'Highlights' (3-4 bullet points), developing an accessible, third-person eTOC blurb, and designing a single-panel Graphical Abstract. Essential for late-stage manuscript polishing and maximizing scientific impact.

A comprehensive guide for researchers writing about Manufacturing and Service Operations Management (M&SOM). It mandates that the method chosen must be dictated by the core operational decision. Provides detailed frameworks for both the analytical lane (e.g., stochastic optimization, queueing theory, game theory) and the empirical/causal lane (e.g., Difference-in-Differences, Instrumental Variables), ensuring the research delivers actionable operational insights.

A guide for developing rigorous analytical or stochastic models for Manufacturing & Service Operations Management (M&SOM). It emphasizes translating operational decisions, objectives, and uncertainties into explicit structural results, such as policy forms (e.g., order-up-to levels) and comparative statics, ensuring the core insight remains operational rather than merely descriptive.

A comprehensive guide detailing the high standards required for presenting security evidence at major conferences. It covers methodologies for proving claims—including attacks, defenses, and detection systems—across live internet measurements, controlled testbeds, and emulation. Key requirements include adaptive adversary modeling, rigorous measurement hygiene, and adherence to ethical review protocols to ensure results are reproducible and relevant to real-world networks.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).