Skill UI

This guide provides authors with best practices for improving the reproducibility and verifiable availability of communication experiments for submission to JOC. It clarifies the journal's current status regarding artifact badges, offering actionable advice on openly sharing code, data, and simulation parameters to enhance credibility while maintaining academic rigor.

This guide details the process of preparing complex research artifacts (simulators, configurations, traces, and scripts) for post-acceptance evaluation, especially for microarchitecture papers. It outlines best practices for achieving ACM badges like 'Available' and 'Reproducible,' focusing on structuring the code base with tiered entry points (smoke, key, full) and handling dependencies, licensed data, and simulation time constraints.

A comprehensive guide for authors preparing accepted academic papers for artifact evaluation at major conferences (e.g., NSDI). It details how to package code, traces, and testbed recipes to ensure permanent public availability, achieve recognition badges (Functionality, Availability, Reproducibility), and meet community award criteria. Learn best practices for reproducibility and structured submission.

A comprehensive guide to interpreting the structural mechanics of large-scale, multi-deadline academic conferences (like NSDI). It details the difference between acceptance, one-shot revision, and rejection, explaining the strategic implications, associated bans, and the required scope for resubmission. Learn how to decode reviewers' feedback, identify critical baselines, and manage your research workflow across multiple cycles.

A comprehensive guide for ensuring robust scientific reproducibility in robotics research (RSS). It structures evidence into distinct 'Replication Tiers' (e.g., Rerunnable, Auditable), distinguishing between the computational half (code, seeds) and the physical half (robot, environment, human protocol). It mandates the use of platform ledgers and raw trial logs to convert claims of trust into verifiable, auditable evidence.

A comprehensive guide for submitting networking research artifacts for SIGCOMM conferences. It dictates how to package code, downscale topologies, substitute traces, and pin environments so that reviewers can independently verify and reproduce the paper's headline results, establishing verifiable scientific claims.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.

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.

This specialized skill is designed to analyze complex security flaws involving forced authentication (coercion) and identity relay chains. It guides users to trace and document the complete, minimal path from the initial coercion trigger (e.g., a specific service or protocol edge) through intermediate hops, to the final service that accepts the relayed credentials. Use this for deep security research, forensics, and documenting the decisive sequence leading to privilege escalation or code execution.

Policy and workflow for reviewing AI-generated code before merge or execution. Covers AST-based static checks, dependency advisories, isolated behavior tests, and evidence collection with language-specific coverage limits.