Skill UI

This guide helps researchers determine the most appropriate venue (e.g., SIGMETRICS, NSDI, IMC, NeurIPS) for their computer systems performance evaluation research. It emphasizes that contributions must be rigorous performance-evaluation results—such as proven models, principled measurements, or guaranteed learning algorithms—rather than merely built systems or pure network measurements.

This skill provides a comprehensive project management framework for submitting research papers to SIGMETRICS. It guides users through the complex, multi-stage process, including evidence lock, abstract registration, handling rolling deadlines, one-shot revisions, and final publication preparation. It emphasizes backward planning heuristics and mitigating common academic failure modes.

A comprehensive guide for authors revising performance-intensive academic papers, such as those for SIGMETRICS. It emphasizes the need to state a precise performance contribution on the first page, explicitly detail all modeling assumptions, and pair every quantitative claim with proportional evidence (proofs, simulations, and measurements).

A specialized guide for structuring the literature review of a sociological methods manuscript. It teaches authors how to position their contribution not against the topic, but against established methodologies. Users learn to map their work against prior estimators, cross-discipline techniques (statistics, econometrics, psychometrics), and competing approaches to maximize academic credibility.

A comprehensive guide for designing rigorous Monte Carlo simulation studies, particularly for methods-heavy academic submissions (e.g., SMR). This guide covers specifying the Data Generating Process (DGP) space, selecting non-negotiable competitor methods, choosing appropriate performance metrics (e.g., power, bias, coverage), and critically reporting the limitations and failure regimes of the proposed method to ensure maximum credibility and reproducibility.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

A comprehensive guide for authors submitting technical papers to top-tier conferences, detailing how to effectively partition content between the main paper body and supplementary artifacts. It emphasizes the critical rule that all decision-making evidence (e.g., headline results, key performance metrics) must reside within the limited page budget of the main submission, ensuring the work is judgeable even without accessing raw data.

A comprehensive guide for writing the evaluation section of a systems paper for top-tier conferences. It teaches authors how to map every technical claim to verifiable experiments, establish fair state-of-the-art baselines, utilize mixed workloads (microbenchmarks, E2E applications, and traces), and honestly report critical metrics like performance tails, variance, and overheads. The goal is to maximize credibility and withstand rigorous peer review.

A comprehensive guide for authors aiming to submit manuscripts to 'Studies in Science of Science.' It details the journal's focus areas, such as science metrics, knowledge networks, and technology policy, emphasizing the need for deep theoretical and methodological contributions. The skill helps authors optimize structure, avoid common rejection traps, and ensure adherence to academic publishing standards.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

A comprehensive tool for assessing whether an econometric manuscript is suitable for submission to The Econometrics Journal (EctJ). It guides authors through evaluating methodological novelty, theoretical rigor, structural compliance, and adherence to high academic standards, covering everything from new estimators and inference procedures to house style and potential desk-reject risks. Use this skill when targeting a highly specialized economic journal or needing to re-frame an applied study into a theoretical methods contribution.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).