Skill UI

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.

This guide provides a comprehensive framework for positioning mobile and embedded systems manuscripts for ACM MobiSys. It details venue fit, contribution framing, necessary real-device evidence, and how to differentiate the work from related conferences (e.g., MobiCom, UbiComp). Use this when your research value depends on surviving real-world device constraints, sensing, or edge computing environments.

A guide for authors needing to precisely position their theoretical contribution against existing literature. It teaches how to identify the 'delta'—the exact difference (e.g., weaker assumption, new environment, full characterization)—relative to the single closest academic result, rather than making vague claims about the field as a whole.

This guide provides comprehensive best practices for ensuring scientific reproducibility in technical papers, particularly for electronics and information processing fields. It covers the critical pillars of documentation: environment setup, data sourcing and preprocessing, full algorithmic disclosure (including pseudo-code and hyperparameters), and robust statistical evaluation. By adopting these standards, authors can transform their work from potentially unverifiable results into credible, verifiable scientific contributions suitable for top-tier journals.

This skill helps authors determine if their research, which focuses on the complex interface between agricultural management and environmental outcomes, is appropriate for the journal 'Agriculture, Ecosystems & Environment.' It details the required scope, from greenhouse gas fluxes and biodiversity to soil and water quality at the field-to-landscape scale, emphasizing the necessity of environmental outcome quantification and sound ecological methodology.

A comprehensive guide for authors aiming to publish agronomy or farming systems research in journals focused on sustainability. It outlines the critical need for making sustainability contributions explicit, measurable, and evidence-based (environmental, economic, and social). Use this tool to reframe your methodology, scope, and findings to meet rigorous academic standards for sustainable development.

This guide helps authors determine if their work meets the high standard required by the Annual Review of Environment and Resources (AREAR). It focuses on synthesizing diverse environmental fields (climate, water, biodiversity, energy) into a field-defining, authoritative review, rather than presenting primary data or a simple literature summary. Use this when proposing or checking the scope for an invited, synthesizing review.

This guide details best practices for ensuring scientific rigor and reproducibility in complex system research. It outlines the necessity of capturing the full execution state—including hardware steppings, BIOS revisions, kernel configurations, toolchain versions, and dataset provenance—to prevent data decay and validate findings against environmental drift.

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 set of reusable patterns for structured incident triage in AWS environments. Includes pre-built Logs Insights queries for detecting errors, latency spikes, cold starts, and OOM events. Also provides systematic correlation methods linking CloudWatch alarms to deployment events, and a decision tree to narrow the blast radius scope from account to resource.

This guide helps academics determine if their manuscript aligns with the scope of China Industrial Economics, or if it should be redirected to more specialized journals (e.g., Economic Research, Management World). It provides criteria based on empirical rigor, policy quasi-experiments, and specific research focus (industry, enterprise, environment).

This comprehensive guide outlines best practices for achieving rigorous reproducibility in complex AI and knowledge graph research. It details how to pin every component—from tokenizers and KGs to LLM prompts and environment settings—ensuring that results are fully documented and verifiable by peers, even when proprietary data is involved.