Skill UI

Guides the rigorous development of theoretical models and research frameworks for Production and Operations Management (POM) manuscripts. It covers analytical, empirical, and behavioral tracks, ensuring that all assumptions and mechanisms are deeply grounded in real-world operational constraints and decision-making processes, thereby building a defensible academic argument.

This skill helps authors determine the suitability of their research paper (analytical, empirical, or behavioral) for prestigious Operations Management journals like Production and Operations Management (POM). It provides detailed guidance on required scope, methodological rigor (e.g., identification strategies, model assumptions), structural requirements, and common pitfalls to ensure maximum publication potential.

A comprehensive guide for designing conceptual figures for highly theoretical manuscripts (e.g., Psychological Review). This skill teaches how to create two types of non-experimental exhibits: structured model/architecture diagrams and simulation-of-model-behavior plots. It emphasizes rigorous academic standards, including defining constructs vs. processes, distinguishing predictions from fits, and ensuring figures are self-contained and diagnostic.

This guide assists researchers in the rigorous construction of psychological theories and formal models for academic manuscripts. It provides a step-by-step methodology to move from initial intuition to an explicit, testable theoretical structure. Users learn to define core assumptions, specify underlying causal mechanisms, and formalize models using mathematical equations or precise conceptual frameworks, ensuring the derived model behavior is the central contribution.

Guides researchers on transforming raw econometric output into publication-ready, self-contained exhibits suitable for high-impact academic journals. Covers strict formatting, page-cap discipline, and structuring complex market behavior, welfare impacts, and elasticity matrices to ensure the core argument is clear and concise.

A comprehensive guide detailing the required structure and technical rigor for writing papers in sensing and embedded systems (SenSys). It mandates replacing vague adjectives with concrete, measured metrics (e.g., energy in joules, latency in milliseconds) and structuring the argument around a clear 'pain $\to$ gap $\to$ mechanism $\to$ measured behavior' arc on the first page. Essential for academic researchers and engineers.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

This guide provides a rigorous methodology for achieving scientific reproducibility in technical and user experience papers (e.g., UIST). It details three essential 'ledgers': the Build Ledger (pinning software, hardware, and environment dependencies), the Measurement Ledger (defining precise protocols for metrics like latency and accuracy), and the Study Ledger (documenting human evaluation tasks). Following these protocols ensures that external researchers can accurately rebuild the artifact and validate reported results.

A comprehensive guide detailing the mandatory and optional evaluation phases for academic security artifacts, such as those required for USENIX Security. It covers Phase-1 (availability) checks and Phase-2 (functionality, reproducibility) requirements. Provides best practices for structuring code, managing technical hazards (e.g., dangerous-by-default behavior), and ensuring verifiable, reproducible results for peer review.

A comprehensive framework for ensuring reproducibility in Web Search and Data Mining (WSDM) research. It addresses unique challenges associated with behavioral data, covering data provenance, temporal splitting discipline, bias assumption reporting, statistical rigor (seeds, variance), and the 'honesty ladder' for non-reproducible results, helping researchers publish robust and verifiable work.

This guide provides detailed stylistic advice for authors submitting papers to WSDM. It emphasizes structuring work around observed user and web behaviors, adopting a self-contained voice that preemptively addresses reviewer objections, and ensuring technical precision in all sections, including ethics and limitations. It transforms raw research into a publication-ready, principle-grounded narrative.

A specialized skill for analyzing client-side persistence mechanisms, including cookies, localStorage, sessionStorage, IndexedDB, and Service Workers. It is crucial for security testing and CTF environments where application behavior or critical decision branches are determined by stored session state, cached data, or local client-side variables. Use this to trace the smallest stateful sequence leading to a decisive outcome.