ecai-experiments
From the author
Use 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.
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-experiments -a claude-code
Installs to .claude/skills/ecai-experiments/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
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Similar skills
Closest to this one by what they do and when they are used.
- eacl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance floors, human-evaluation agreement, data-contamination controls, ablations, and error taxonomies, so that every stated result is measured rather than asserted.Science★ 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.Testing★ 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.Science★ 1.3K
- popl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing the empirical component of a POPL paper — deciding whether evidence should be a mechanization, a prototype, case studies, or benchmarks; reporting proof effort and case-study coverage honestly; and keeping performance numbers in a supporting role so the formal claim stays the paper's center of gravity.Science★ 1.3K
- uai-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse 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.Other★ 1.3K
- ecai-submissionbrycewang-stanford/Awesome-Journal-SkillsUse when auditing an ECAI submission for readiness — the abstract-then-full-paper two-deadline flow, the tight 7-page body budget, double-blind anonymity, the correct template/publisher regime (ecai.cls/FAIA for a standalone ECAI vs ijcai.sty/IJCAI proceedings for the joint IJCAI-ECAI 2026), the per-author submission cap, the ethics statement, and desk-reject triage before the AoE cutoff.Science★ 1.3K
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