Skill UI

This comprehensive guide helps authors assess if their electrochemistry manuscript (covering batteries, fuel cells, supercapacitors, etc.) is suitable for the Journal of Power Sources. It instructs users on how to reframe purely material-focused advances into compelling device-and-electrochemistry stories, emphasizing cell-level performance, degradation analysis, and rigorous testing standards.

A comprehensive checklist for auditing submissions to top-tier data mining conferences like KDD. This guide covers critical areas including track declaration, OpenReview profile completeness for all authors, adherence to strict page limits, double-blind anonymity rules, and quantifying post-launch performance evidence. Essential for maximizing acceptance chances.

A comprehensive guide for integrating Markstream, a high-performance Markdown renderer, into modern frontend frameworks like React, Vue, Svelte, and Next.js. It handles complex streaming scenarios, ensuring minimal peer dependencies, correct CSS ordering, and proper Server-Side Rendering (SSR) boundaries for dynamic content and AI chat interfaces.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

Provides a comprehensive methodology for evaluating mobile computing systems. It mandates rigorous, real-device testing, focusing on instrumenting energy consumption, thermal behavior, latency distribution (tails), and memory limits. This guide ensures academic submissions are credible, repeatable, and compare performance against robust, tuned baselines.

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 skill automates the creation of professional, multi-element Excel dashboards from raw CSV or tabular data. It structures the output with a dedicated 'Dashboard sheet' that serves as the primary landing view. Features include formula-driven KPI cards, linked charts, sparklines, and semantic conditional formatting. It is ideal for generating executive, analytics, or performance dashboards, ensuring that the user sees immediate, actionable insights without needing to navigate raw data sheets.

This comprehensive guide assists reviewers and authors in determining the most appropriate venue for programming language research within the SIGPLAN community. It provides a detailed 'center-of-mass' test, helping categorize contributions based on whether they emphasize metatheory (POPL), performance engineering (PLDI), functional techniques (ICFCP), or the broad design scope characteristic of OOPSLA. It guides the decision-making process for academic submissions.

A comprehensive guide for constructing rigorous and structured exhibits (tables, figures, theorems) for Operations Research (OR) manuscripts, adhering to INFORMS house style. It details requirements for formal statement layout, comparison tables against prior work, computational results, and performance plots, ensuring maximum academic rigor and reproducibility.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.

A comprehensive guide for researchers to determine the optimal academic venue (e.g., PODS, SIGMOD, VLDB, ICDT) for their paper. The guide helps distinguish between contributions that are foundational theorems (theory) and those that are performance-measured systems artifacts, ensuring proper scope definition before submission.

This guide outlines the necessary steps to ensure that parallel computing performance papers are scientifically reproducible. Since parallel results depend heavily on machine architecture, topology, and runtime conditions, proper documentation must cover everything from CPU/GPU specifications and interconnects to thread pinning policies, random seeds, and compiler flags. The focus is on proving the scaling trend, not just the absolute numbers, allowing results to hold true across different hardware platforms.