Skill UI

A comprehensive audit guide for submitting full papers to IEEE VIS. It covers the complex, multi-stage submission process, requiring authors to adhere to journal-quality standards (IEEE TVCG). The guide details the strict deadlines for abstracts, full papers, and supplemental materials, along with critical mechanics like the 9+2 page budget, proper society/track selection, and advanced double-blind anonymization techniques.

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.

A detailed guide for revising academic papers, specifically tailored for systems and database conferences like VLDB. It emphasizes establishing a strong 'page-one contract,' quantifying all performance claims with scoped metrics and baselines, and maintaining rigorous candor regarding design trade-offs and terminology throughout the manuscript.

This comprehensive guide provides researchers with structured methodologies for designing and auditing Computer Vision (CV) experiments for major conferences like WACV. It details the critical differences between the Applications and Algorithms tracks, emphasizing the need to report performance under real deployment constraints (e.g., power, latency) and ensuring fair, rigorous comparisons with baselines. Ideal for revising submissions to meet high review standards.

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.