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Load Test and Scale Vercel Deployments
vercel-load-scale
jeremylongshore/claude-code-plugins-plus-skills
80
This guide provides comprehensive methods for load testing and capacity planning of Vercel serverless deployments. Learn how to use professional tools like k6 and autocannon to simulate realistic traffic spikes, measure latency (P95, P99), analyze cold start impact, and tune concurrency settings (Fluid Compute) to ensure optimal application performance and scalability under heavy load.
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DARS Conference Submission Strategy Guide
international-symposium-on-distributed-autonomous-robotic-systems
brycewang-stanford/Awesome-Journal-Skills
385
This skill guides authors preparing multi-robot systems papers for the DARS conference. It provides strategic advice on conference fit, required evidence (e.g., scalability, decentralization), topic scope, and manuscript framing to meet the specific expectations of distributed autonomous robotic systems reviewers. Use this to validate your submission strategy before writing the final draft.
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Designing Rigorous Parallel System Evaluations
ppopp-experiments
brycewang-stanford/Awesome-Journal-Skills
493
This comprehensive guide outlines the rigorous methodology required for designing high-impact evaluations of parallel systems and algorithms for top-tier conferences. It stresses that contributions must demonstrate the 'twin bar': both concurrency correctness (e.g., linearizability) and measurable scalability (e.g., strong vs. weak scaling). It covers best practices for establishing strong baselines, managing measurement hygiene (variance, pinning), and anticipating advanced reviewer scrutiny in HPC.
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Designing Rigorous System Benchmark Experiments
vldb-experiments
brycewang-stanford/Awesome-Journal-Skills
400
A comprehensive guide for designing and presenting highly rigorous technical evaluations for database or system papers. It covers establishing baseline necessity, proving mechanism causality through ablation, testing scalability across data sizes, and mandating the disclosure of loss cases to satisfy skeptical academic reviewers.
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