Skill UI

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

This guide provides comprehensive best practices for authors to enhance the reproducibility of technical research papers (especially in networking and computer science). It covers documenting proofs, pinning simulation setups, reporting parameters, and best practices for releasing code and datasets, transforming a paper from a claim into a verifiable scientific artifact.

This comprehensive guide provides best practices and an audit checklist for designing robust, real-world experiments in mobile and wireless communication systems. It emphasizes moving beyond simulations, detailing necessary RF setups, quantifiable energy metrics, and reporting statistical distributions to ensure high academic credibility.

A comprehensive guide for researchers detailing how to establish rigorous provenance for on-device performance measurements (e.g., energy, latency, accuracy). It mandates recording all necessary context—including device model, SoC, OS build, framework versions, power instrument setups, and thermal conditions—to ensure results are verifiable and reproducible across different hardware or environments for academic publication.

This guide teaches the 'trust-minimum principle' for writing methods sections in short scientific letters (like PRL). It instructs authors on how to rigorously partition their work, keeping only the essential, trust-bearing details (setup, key controls, uncertainties) in the body, while relegating full derivations and extensive apparatus details to the Supplemental Material (SM). This ensures brevity without sacrificing scientific credibility.

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

This guide provides rigorous best practices for developing and presenting theoretical economic models for top-tier journals. It emphasizes framing results with a clear economic payoff, maintaining minimal model setups, and ensuring that complex, full proofs are relegated to the online appendix. The goal is to ensure the primary economic insight remains highly visible and actionable, rather than being buried in algebraic machinery.

This comprehensive guide outlines best practices for achieving high levels of scientific reproducibility in systems papers, especially for cloud measurement studies. It teaches authors how to map every claim in their paper to verifiable evidence, detailing the entire testbed setup, reproducing tail latency and costs accurately, and maintaining consistency between reported numbers and artifacts.

A comprehensive methodological guide for designing rigorous academic studies in accounting. It details advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and experimental setups—to establish credible causal identification. Use this guide when your research challenge is solving endogeneity or isolating a specific economic channel from complex, non-random variables.

A comprehensive guide to vcpkg, a native C/C++ package manager. It covers project setup, dependency management, and cross-compiling for robust builds. The guide details best practices for Manifest mode, CMake integration, and Visual Studio setup, ensuring reproducible and isolated build environments.

This skill establishes the root directory for a collaborative workshop, serving as a centralized project hub. It manages two distinct paths: using an existing local directory (Path A), or initializing a new private GitHub repository (Path B). It scaffolds the necessary structure—including `desks/`, `bench/`, and `CAIRN.md`—while strictly preserving existing work and preventing repository nesting. Ideal for standardizing project setup and maintaining collaboration standards.