imc-experiments
De la autor
Use when designing or auditing ACM IMC measurements, covering representative vantage points, honest ground truth, longitudinal design for a moving Internet, safe and ethical active measurement, coverage-bias quantification, provenance pinning, and matching the measurement to the shape of each claim about the real Internet.
Instalare
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill imc-experiments -a claude-code
Se instalează în .claude/skills/imc-experiments/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 88
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- imc-writing-stylebrycewang-stanford/Awesome-Journal-SkillsUse when drafting or revising the prose of an ACM IMC measurement paper, covering the first-page measurement finding, making vantage points and methodology auditable, longitudinal framing for a moving Internet, arguing limitations rather than reciting them, writing the Ethics section early, and acmart page-budget discipline.Redactare★ 1.3K
- imc-related-workbrycewang-stanford/Awesome-Journal-SkillsUse when writing or auditing the related-work and positioning of an ACM IMC paper, covering the measurement literature lanes, positioning against prior datasets and vantage points, delta-first framing, citing the datasets and tools you build on, distinguishing IMC from SIGCOMM/NSDI/PAM prior art, and keeping self-citations double-blind.Știință★ 1.3K
- icdm-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned baselines, ablations that isolate the named mechanism, scalability curves that test scale claims, and discovery-validity checks that separate real findings from evaluation artifacts.Testare★ 1.3K
- icse-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.Testare★ 1.3K
- socc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.DevOps★ 1.3K
- atc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting, workload realism, and honest cost accounting.Știință★ 1.3K
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