Skill UI

This skill helps authors determine if their manuscript is suitable for the International Conference for High Performance Computing, Networking, Storage and Analysis (SC). It provides comprehensive guidelines on framing the work, establishing the evidence bar (requiring scale, performance portability, and systems impact), and positioning the contribution for a top-tier HPC flagship venue. Use it for strategic venue selection and manuscript re-framing.

This skill guides authors preparing multi-robot systems papers for the DARS conference. It provides strategic advice on conference fit, required evidence (e.g., scalability, decentralization), topic scope, and manuscript framing to meet the specific expectations of distributed autonomous robotic systems reviewers. Use this to validate your submission strategy before writing the final draft.

Provides a comprehensive guide for packaging complex research artifacts, integrating hardware, firmware, and datasets. It outlines best practices for achieving academic badges (e.g., Available, Functional, Reusable) and winning the Best Research Artifact Award. Learn how to structure analysis paths, manage raw traces, and ensure reproducibility across different evaluation environments.

A comprehensive guide for designing and auditing rigorous experiments for sensing systems and information processing claims. It emphasizes the necessity of measuring performance on real hardware (not simulation), accurately instrumenting ground truth, quantifying energy consumption, latency, and physical footprint, and establishing fair, skeptical baselines. This ensures scientific rigor and reproducibility in research submissions.

A comprehensive guideline for establishing and documenting the reproducibility of complex sensor-systems artifacts. It covers necessary components such as firmware sources, detailed hardware Bills of Materials (BOMs) and board files, raw sensor traces, ground truth data, and calibrated measurements. Use this guide to ensure that scientific claims can be verified by a competent reader, detailing the exact dependencies on physical equipment and software toolchains.

This guide provides structured rules for authors writing sensor-systems or information-processing papers. It emphasizes leading with the physical sensing problem, requiring real-hardware measurements for energy, latency, and ground truth, and adhering to strict academic formatting constraints. Use this when revising submissions to ensure technical depth and practical deployability.

This guide provides rigorous guidelines for structuring and validating experimental results in computer architecture papers. It covers essential aspects like declaring simulation methodology contracts, ensuring result reproducibility through manifest files, selecting representative workloads, and performing detailed attribution and sensitivity analyses to distinguish true architectural gains from modeling artifacts.

A comprehensive guide on achieving scientific reproducibility for complex computer architecture and system simulations. It details how to manage and pin all dependencies—including simulator commit hashes, local patches, workload binaries, hardware states, and post-processing scripts—to ensure that every reported figure and number can be accurately regenerated, regardless of time or environment changes.

This skill provides a framework to organize and structurally review readiness evidence for major international standards, including ISO 13485 (Medical Devices), ISO 17025 (Testing Labs), and ISO 15189 (Medical Labs). It helps users define the scope, manage controlled documents, establish traceability, and compile preparatory material for substantive human expert review. It is explicitly a preparation tool, not an audit, certification, or legal determination system.

Provides comprehensive guidelines for achieving high reproducibility and verifiability in academic research submissions, particularly for software analysis, fuzzing, and bug finding. It details the necessity of pinning substrates (like benchmark versions and program SHAs), fixing stochastic parameters (seeds and budgets), and mapping every empirical claim to verifiable evidence to meet stringent academic evaluation criteria.

A comprehensive guide for authors preparing artifacts (code, data, models, etc.) for submission to the Journal of Computer-Aided Design & Computer Graphics (JCAD&CG). It details best practices for ensuring research reproducibility, writing honest availability statements, and managing artifacts during the double-blind review process, focusing on ethical standards regardless of specific journal policy.

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.