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 teaches researchers how to conduct an exhaustive "reinvention audit" for academic papers, specifically in the database field. It covers searching deep historical literature (pre-2000), modern conference venues (SIGMOD, ICDE), and industrial open-source systems (PostgreSQL, RocksDB) to ensure true novelty and properly position the contribution delta.

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.

A comprehensive audit guide for authors submitting to WACV. It covers critical submission decisions, including choosing between Round 1 or Round 2, selecting the appropriate Applications or Algorithms track, adhering to the 8-page limit, maintaining double-blind anonymity, and ensuring all coauthors have valid OpenReview profiles to prevent desk rejection.

Provides comprehensive guidelines for designing and auditing academic tables and figures, specifically for development economics journals. It teaches how to translate statistical coefficients into actionable policy magnitudes, validate research design (e.g., balance checks, pre-trends), and adhere to strict page limits while maintaining clarity for both economists and policymakers.

A comprehensive audit guide for submitting research papers to major Web Conferences (WWW). It details critical, irreversible steps such as author list freezing, restrictive page arithmetic (12 pages total), track selection protocols, anonymity checks, and managing submission caps. Essential reading for research teams preparing for demanding academic deadlines.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

A comprehensive guide for auditing research papers submitted to the WSDM conference. It details critical constraints, including the no-rebuttal policy, strict 9-page budgets (including appendices), and stringent hybrid anonymization requirements. Use this guide to preemptively identify and correct submission flaws before the deadline.

This skill performs a rigorous audit of legal journal footnotes, verifying the existence, pinpoint accuracy, Bluebook formatting, and direct textual support for every citation. It is essential for manuscript preparation, particularly before a source-pull audit, ensuring the piece survives scholarly review without citation errors or integrity issues.

A comprehensive audit guide for authors submitting papers to the ACM Symposium on Theory of Computing (STOC). This guide details the critical requirements, including the single-column 11-point format, the double-blind anonymity rules, the importance of the abstract, table of contents, and the first twelve pages (the 'reading contract'). It covers formatting, publication policies, and the submission workflow to maximize review impact.

Provides a comprehensive, quantitative auditing system for AI agents, skills, and commands. It evaluates these components against industry best practices using a multi-criteria weighted scoring model and assign a production readiness grade (A-F). Ideal for validating complex AI systems before deployment, identifying gaps, and ensuring robust, maintainable code.

This tool provides a comprehensive, multi-faceted audit of a codebase's health. It scores the project across critical areas like security vulnerabilities, exposed secrets, dependency risks, and structural integrity. Ideal for senior engineers performing pre-commit or pre-release quality assessments to identify weak spots and generate a prioritized remediation plan.