uai-experiments
De la autor
Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
Instalare
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-experiments -a claude-code
Se instalează în .claude/skills/uai-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.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
Niciun avertisment.
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.
- aaai-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.Altele★ 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
- 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
- ecai-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAI's breadth (theory/KR, planning/search, ML, multi-agent, applied), fair baselines, seeds and spread, honest ablations, and provenance, all supporting a claim inside a 7-page body.Știință★ 1.3K
- fse-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and mixed-methods rigor, contamination-aware LLM ablations, provenance for mining studies, and matching evidence to the shape of each software-engineering claim.Altele★ 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
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te