pods-experiments
From the author
Use when designing or auditing the analytical "evidence" of an ACM PODS paper — worst-case and average-case analyses, matching upper and lower bounds, dichotomy completeness, correct complexity assumptions, and the occasional empirical validation when a theory paper claims practicality — matching the rigor to the shape of each theoretical claim.
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-experiments -a claude-code
Installs to .claude/skills/pods-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
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- podc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when building the evidence for an ACM PODC paper — where "evidence" is a proof, not a benchmark. Covers matching upper and lower bounds, tightness arguments, model and assumption stress-tests, adversary-strength calibration, and the honest, clearly-optional role of any simulation in a distributed-computing-theory paper.Testing★ 1.3K
- pods-submissionbrycewang-stanford/Awesome-Journal-SkillsUse when auditing an ACM PODS research submission for EasyChair readiness, covering the chosen cycle's abstract-then-paper deadlines, the acmsmall[review,anonymous] format and 15-page budget, the at-submission proof appendix, lightweight double-anonymity, the declared conflicts of interest, PACMMOD-track publication, and desk-risk triage before the AoE cutoff.Science★ 1.3K
- colm-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical core of a COLM paper — contamination analysis for evaluation data, fair baselines under matched prompting and compute, pinned model versions and decoding parameters, uncertainty over runs and samples, scaling coverage, and honest reporting of API-model comparisons.Science★ 1.3K
- chi-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work rigorous and auditable, reporting participants and ethics properly, and avoiding the ADR-Data and ADR-Method screening grounds.Science★ 1.3K
- 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
- cikm-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical program of a CIKM paper — matching evidence to the claim's lanes across retrieval, mining, and knowledge-management evaluation cultures, choosing datasets and baselines that survive a blended panel, isolating the boundary mechanism, and meeting applied-track deployment-evidence bars.DevOps★ 1.3K
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