oopsla-experiments
Use when designing or auditing the evaluation of an OOPSLA paper — matching evidence type to claim type across the venue's spread (benchmarks, corpus studies, case studies, user studies, mechanized proofs), building baselines and workloads that survive the SIGPLAN checklist, and sizing experiments to the round calendar.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill oopsla-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# OOPSLA Experiments OOPSLA's evaluation question is not "is there a big table?" but "does the evidence *type* match the claim *type*?" The venue's published scope runs from mathematical formalisms to empirical studies, and its exemplars span benchmark suites, measurement methodology, corpus mining, and language experience reports (`resources/exemplars/library.md`) — so the first design act is choosing the right instrument, and the second is executing it to the SIGPLAN Empirical Evaluation Guidelines standard that reviewers apply checklist-in-hand (`oopsla-reproducibility` operationalizes the pillars). ## Claim-type → evidence-type routing | Claim type | Primary evidence | Common OOPSLA failure | | --- | --- | --- | | "Faster / cheaper" | Benchmarks vs strongest baseline, variance reported | Weak baseline; startup vs steady-state conflated | | "More expressive / safer" | Formal result + programs witnessing the boundary | Expressiveness asserted by example only | | "Programmers benefit" | User study or field data with a design | Anecdote from the authors' own use | | "Occurs in practice" | Corpus study with stated selection rule | Convenience sample of famous repos | | "The design g
- Claim-type → evidence-type routing
- Baselines and workloads that survive scrutiny
- Sizing experiments to the round calendar
- Analysis floor
- Output format
What does the oopsla-experiments skill do?
Use when designing or auditing the evaluation of an OOPSLA paper — matching evidence type to claim type across the venue's spread (benchmarks, corpus studies, case studies, user studies, mechanized proofs), building baselines and workloads that survive the SIGPLAN checklist, and sizing experiments to the round calendar.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill oopsla-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.