Skill UI

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

A guide for articulating and bounding a methodological contribution in social science research. It teaches authors how to explicitly state the problem, the failing incumbent method, the mechanism of the fix, and the associated costs, ensuring the claim is proportional, specific, and actionable rather than overly general.

This guide outlines the best practices for preparing system artifacts for academic evaluation, following the ACM Artifact Review and Badging scheme. It details how authors must ensure reproducibility, especially for tail latency and cost results, by providing scaled-down testbeds, comprehensive documentation, and clear provenance tracking to satisfy conference evaluators.

A comprehensive guide for drafting the critical author response (rebuttal) for the SoCC conference. Since there is no major revision round, this single, high-stakes turn must persuasively address concerns from both systems and data/measurement reviewers. It emphasizes correcting factual errors, supplying missing numbers (like tail latency and cost), and maintaining strict dual anonymity.

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.

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.

This guide details the rigorous academic review process for ACM Symposium on Cloud Computing (SoCC) submissions. It explains the dual-anonymous, two-round review structure, the importance of incorporating both systems performance and data measurement rigor (including tail latency and cost), and how the rebuttal period functions. Essential reading for authors aiming to publish in top-tier cloud computing venues.

This comprehensive guide provides strict structural and technical guidelines for authors submitting papers to ACM SoCC. It mandates focusing on cloud-scale problems recognized by operators, emphasizing contributions at the intersection of systems and data. Key requirements include reporting tail latency (p95/p99) and concrete cost models, grounding every claim in measured, real-world evidence, and explicitly stating external limitations.

This workflow performs a comprehensive review against the Azure Well-Architected Framework (WAF) using your existing Infrastructure as Code (IaC) and deployed resources. It systematically checks the five pillars—Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency—identifying architectural risks, drift, and compliance gaps. Findings are automatically logged and converted into actionable GitHub issues for streamlined remediation.

ccboard is a comprehensive TUI and Web dashboard designed for monitoring and managing Claude Code usage. It provides unified visibility into token consumption, detailed cost tracking, session history, and the real-time status of Model Context Protocol (MCP) servers. Use it to analyze usage patterns, manage configurations, and ensure cost efficiency across all projects.

This diagnostic skill audits the entire local setup of Claude Code against several known, complex cache and cost bugs (CC#40524, B4, B5). It checks critical components including version compatibility, sentinel placeholders, session resuming logic, and attribution headers. Use this tool to ensure your development environment is optimized, stable, and minimizes unexpected operational costs and functional regressions.

This skill guides decision-making when resources (time, context, tools) are finite and the search space is infinite. It instructs the user to first establish a clear, explicit 'good enough' aspiration level. Once set, the process involves stopping at the first option that meets this threshold, preventing costly over-optimization and wasted effort.