Skill UI

Provides comprehensive guidelines for writing the "Related Work" and literature review sections for high-level academic publications. It emphasizes demonstrating the paper's unique contribution (delta) by organizing literature along technical threads, ensuring balanced coverage of domestic and international sources, and understanding the distinct requirements for regular papers versus specialized review commentary.

A comprehensive model detailing the rigorous evaluation process for submissions to STOC (Symposium on Theory of Computing). It covers the committee structure, the roles of program committee members and external subreviewers, the key evaluation axes (Correctness, Significance, Breadth, Novelty), the typical timeline, and strategic advice for authors on maximizing their leverage throughout the double-blind review process.

A comprehensive guide for structuring and positioning computer science manuscripts for The Web Conference (WWW). It advises authors on leveraging open-web data, focusing on web systems, web mining, and responsible AI, while adhering to the latest venue-specific standards and evidence requirements.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

A comprehensive guide for authors submitting to UAI (Uncertainty in Artificial Intelligence). It details how to position research by ensuring coverage across diverse academic communities, including Machine Learning, Statistics, Causal Inference, and foundational mathematics. Focuses on establishing the technical 'delta' rather than merely listing citations, helping authors avoid common review failures.

A comprehensive guide for optimizing computer science manuscripts targeting the USENIX Annual Technical Conference (ATC). It covers core systems research fields (OS, networking, storage, security), defining the required evidence bar (built artifacts, clean evaluation), and re-framing the paper's contribution to align with top-tier systems venue expectations.

A specialized guide for assessing whether a storage systems or file system manuscript fits the USENIX FAST conference. It covers venue positioning, required evidence (realistic workloads, fault models), appropriate framing, and comparison methodologies necessary for top-tier systems conference submissions.

A comprehensive guide for positioning research papers for top-tier security conferences like USENIX Security. It covers defining coverage against 'Big-Four' and specialty venues, ensuring novelty across multi-cycle academic pipelines, identifying mechanism-level differentiators, and mastering double-blind citation mechanics for maximum impact.

A comprehensive guide for authors submitting visualization papers to major conferences like IEEE VIS. It outlines the strict rules for partitioning content, ensuring the core argument remains judgeable from the main body while leveraging supplemental materials (video, code, full data) to demonstrate interaction and reproducibility. Essential reading for academic researchers in the field.

This comprehensive guide outlines the rigorous, multi-step process required for authors to finalize and submit their accepted papers for VLDB proceedings. It covers essential technical compliance points, including using the correct volume template, ensuring PDF/A compliance with embedded fonts, adhering to figure legibility rules, and detailing the permanent storage of research artifacts (DOI-backed). It also outlines the associated conference presentation obligations.

This guide provides a structured framework for conducting and presenting robust checks in econometric manuscripts, particularly relevant for studies using developing-country data. It moves beyond simple appendix dumping by organizing tests around potential identification threats and specific data-quality risks (e.g., measurement error, coverage gaps). It helps ensure the core findings are load-bearing against sophisticated academic scrutiny.

webmcpify is a specialized pipeline designed to make existing web applications agent-ready. It extracts user-facing functionality and exposes it as WebMCP tools, allowing AI agents to interact with the application via structured, reliable tool calls rather than fragile DOM scraping. The tool guides developers through detection, inventory, integration, verification, and healing phases to ensure robust AI interoperability.