Skill UI

A comprehensive guide for authors developing systems papers for major conferences (like ATC). It details how to build a reproducible story by pinning all environments (hardware, OS, toolchain, workload, code), providing turnkey one-command execution paths, and preparing a runnable, anonymized artifact package for academic review and evaluation.

A comprehensive guide detailing the end-to-end workflow for submitting research papers to top-tier academic conferences, such as the ACM SIGOPS ATC. It provides a backward planning structure, covering critical stages from initial system freezing and extended abstract writing, through multi-round peer review, rebuttal preparation, artifact evaluation, and final camera-ready submission.

This guide outlines the comprehensive process for submitting and evaluating Computer Aided Verification (CAV) research artifacts. It details the criteria for earning three badges (Available, Functional, Reusable), the two-phase review structure (smoke-test and full review), and best practices for packaging scientific tools, including the use of containers, pinned benchmarks, resource limits, and proof witnesses. This ensures reproducibility and rigor in academic research.

Use this comprehensive guide to manage the entire project lifecycle for a CAV (Computer Aided Verification) paper submission. It provides a backward-planning timeline, covering topics from initial concept fitting and evidence locking to the two-stage review process, optional artifact evaluation, and final camera-ready submission. Essential for managing technical academic deadlines and mitigating common failure modes.

This guide details the process of packaging and evaluating academic artifacts for ACM CCS conferences. It outlines the criteria for earning specific ACM badges (e.g., Functional, Reusable, Results Reproduced) by ensuring that supporting evidence—such as exploits, defenses, and measurement pipelines—is turnkey, reproducible, and rigorously documented for the review committee. It covers best practices for artifact planning, handling sensitive material, and justifying any withheld components.

This guide details the comprehensive workflow for preparing an accepted ACM CCS paper for the final proceedings. It covers critical steps including de-anonymization, applying ACM sigconf templates and rights forms, incorporating shepherd/minor-revision feedback, placing artifact evaluation badges, and coordinating publication disclosure timing. Adherence to these technical and policy checks ensures compliance with ACM standards.

A comprehensive project management skill designed for guiding the entire lifecycle of an ACM CCS conference submission. It maps out critical milestones, including abstract registration, full-paper upload, rebuttal periods, notification, and artifact evaluation. Users can utilize the backward-planning templates and 'big-four' deadline hopping strategies to manage complex academic deadlines effectively.

This guide outlines best practices for packaging research artifacts (prototypes, datasets, instruments, code) for academic review and permanent public release, particularly in HCI venues like CHI. It covers strategies for creating highly reviewable, anonymous submissions (e.g., claims maps, minimal demos) and establishing robust, licensed, permanent archives (e.g., DOIs, OSF), ensuring research reproducibility and auditability.

A comprehensive guide detailing the methodology for packaging, documenting, and staging research artifacts (including code, datasets, knowledge graphs, and demo systems) for CIKM conference submission. It covers best practices for licensing, provenance tracking, versioning, and transitioning artifacts from anonymous review status to citable public releases, ensuring reproducibility and adoptability.

A comprehensive guide for authors submitting academic papers, detailing how to proactively provide reproducible code, datasets, and experimental scripts. This ensures that research results are verifiable and trustworthy, significantly enhancing the credibility of findings in high-tier journals despite the absence of formal artifact evaluation badges.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.