Skill UI

This comprehensive guide outlines best practices for creating and submitting supplementary materials for top-tier academic conferences. It addresses the critical challenges of maintaining strict anonymity, adhering to page limits, and structuring extended proofs, code links, and datasets across multi-round, double-anonymous review cycles.

This comprehensive guide outlines the mandatory steps for transforming an accepted, anonymized academic paper into the final ACM Digital Library Camera-Ready format. It covers de-anonymization, adhering to ACM metadata standards (DOI, ORCID, CCS concepts), integrating reviewer feedback, and ensuring permanent reproducibility of code and traces.

This skill activates 'Steno Mode,' providing highly compressed, expert-level responses by eliminating filler words and superfluous tokens (up to 40% reduction). It maintains absolute technical precision, making it ideal for technical communications like code reviews, debugging Q&A, and architecture summaries. Users can select four defined levels (lite, brief, court, machine) to control the density, with exactness always taking top priority.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

This guide details the strategic placement of content for academic submissions, dividing material into Main Part, Appendix, and Supplementary ZIP. It provides best practices for authors to manage proofs, protocols, and code while adhering to strict page limits and double-blind review constraints, ensuring maximum review integrity and clarity.

A comprehensive guide detailing how to package and present research artifacts—including code, hardware designs, and datasets—for major academic conferences. It outlines best practices for both initial review-time evidence and long-term public reuse, ensuring reproducibility and maximizing the chances of adoption by future builders.

A comprehensive guide for producing the mandatory supplementary video figure for UIST submissions. This material treats the 3-minute video as core evidence, detailing requirements for resolution (1080p/4K), codec (H.264), and anonymity. It guides authors on scripting the video around key claims, ensuring uncut interaction loops, and maintaining academic rigor to convince reviewers of the system's reality.

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 guide for assembling a reproducible data and code package for publishing economic manuscripts, specialized for World Bank Economic Review (WBER) standards. It details best practices for data availability statements, handling restricted microdata, pinning software versions, and ensuring full transparency throughout the analysis pipeline.

A comprehensive guide for preparing replication and transparency packages for World Development manuscripts. It provides checklists and best practices for both quantitative (code reproducibility, data provenance, de-identification) and qualitative (evidence-to-claim trail, methods appendix) research. It addresses ethical constraints and the unique challenges of double-anonymized review.

This workflow performs a comprehensive review against the Azure Well-Architected Framework (WAF) using your existing Infrastructure as Code (IaC) and deployed resources. It systematically checks the five pillars—Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency—identifying architectural risks, drift, and compliance gaps. Findings are automatically logged and converted into actionable GitHub issues for streamlined remediation.

A comprehensive white-box security auditing workflow designed for proprietary source code. It integrates static analysis security testing (SAST) tools like Semgrep and CodeQL to detect common vulnerabilities (e.g., injection, IDOR, crypto misuse), followed by mandatory manual threat modeling and verification steps. Applicable for PR reviews and pre-deployment security checks.