wsdm-experiments
De la autor
Use when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs, baseline selection from recent WSDM/SIGIR/KDD editions, ablations that isolate the mechanism, efficiency reporting, and online-evidence framing.
Instalare
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-experiments -a claude-code
Se instalează în .claude/skills/wsdm-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
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- kdd-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical section of a KDD paper, where evidence combines quality deltas with scalability and efficiency measurements, temporal-leakage-safe splits, mechanism-isolating ablations, tuning-symmetric baselines, and, for the ADS track, post-launch measurement design that survives the desk check.Design★ 1.3K
- osdi-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an OSDI submission — choosing mature baselines and realistic workloads, structuring the section around research questions, measuring scalability and tail behavior, quantifying the design's costs, and fitting the evidence into the 12-page reviewed body.Design★ 1.3K
- uist-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of a UIST paper — choosing among technical benchmarks, controlled comparisons, usability walkthroughs, expert sessions, and demonstration applications, matching evaluation shape to the systems claim, and avoiding the ritual study that proves nothing the paper asserts.Știință★ 1.3K
- acl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.Știință★ 1.3K
- ase-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an ASE (IEEE/ACM Automated Software Engineering) paper, covering real subject systems, fair runnable tool baselines, task-matched effectiveness metrics, ablations that isolate a learned component, oracle and correctness validation, contamination-aware LLM handling, and provenance for mining.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
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