oopsla-reproducibility
Use when hardening an OOPSLA paper's empirical claims to the SIGPLAN Empirical Evaluation Guidelines — managed-runtime measurement discipline, warmup and variance reporting, corpus and benchmark provenance, environment pinning, and a Data-Availability Statement that the eventual artifact can actually honor.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill oopsla-reproducibility --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 Reproducibility OOPSLA carries a particular historical burden here: the venue itself published the papers showing that sloppy runtime measurement produces wrong conclusions — Georges, Buytaert & Eeckhout's statistical-rigor paper (OOPSLA 2007) and the DaCapo suite's methodology argument (OOPSLA 2006); see `resources/exemplars/library.md`. Reviewers steeped in that lineage apply the SIGPLAN Empirical Evaluation Guidelines (`sigplan.org/Resources/EmpiricalEvaluation/`) as a working checklist, and the two-round model gives them a Minor/Major Revision lever to demand rigor rather than merely complain about it. Reproducibility work done before Round N is cheaper than the revision it preempts. ## The four guideline pillars, operationalized | Pillar | Reviewer question | Concrete obligation in the paper | | --- | --- | --- | | Clear claims | What exactly is asserted, on what workloads, on what hardware? | Claims scoped with population, platform, and configuration | | Suitable comparison | Is the baseline the strongest sensible one, correctly configured? | Baseline versions, flags, and tuning documented | | Principled benchmarks | Why these programs/corpora and not cherry-picked o
- The four guideline pillars, operationalized
- Managed-runtime and PL-specific traps
- Reproducibility ledger
- Statement discipline
- Pre-round self-audit
- Output format
What does the oopsla-reproducibility skill do?
Use when hardening an OOPSLA paper's empirical claims to the SIGPLAN Empirical Evaluation Guidelines — managed-runtime measurement discipline, warmup and variance reporting, corpus and benchmark provenance, environment pinning, and a Data-Availability Statement that the eventual artifact can actually honor.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill oopsla-reproducibility --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.