eurosys-reproducibility
Use when hardening the reproducibility story of a EuroSys paper — recording hardware and software provenance for every number, taming performance variance with repeated runs and dispersion reporting, versioning workloads and traces, and writing an availability statement that survives both double-blind review and the sysartifacts AEC.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-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.
# EuroSys Reproducibility Use this while experiments are still running — reproducibility retrofitted in deadline week is transcription, not engineering. The venue context: EuroSys papers live on measured performance claims, the community runs a badge-granting artifact evaluation (`sysartifacts.github.io`), and SIGOPS has publicly digested five years of EuroSys AE lessons (sigops.org blog, 2025; rendered 2026-07-08). A paper whose numbers cannot be regenerated by its own authors three months later fails both review-time scrutiny and post-acceptance AE. ## The provenance ledger Keep one machine-readable record per reported number. Minimum fields: | Field | Why EuroSys reviewers care | |---|---| | Commit hash of system under test | "Which version got 2.1x?" is a real AE question | | Baseline name + version + config | Untuned-baseline suspicion is the venue's default | | Hardware: CPU/RAM/NIC/storage, topology | Systems results rarely transfer across boxes | | OS/kernel, key library versions | Kernel changes move I/O and scheduler numbers | | Workload/trace + generator seed | Trace provenance is checked, not assumed | | Repetitions, warm-up policy | Distinguishes measurement from anecd
- The provenance ledger
- Variance discipline
- Automation floor
- Availability statement, two audiences
- Restricted evidence, stated honestly
- Pre-deadline reproducibility drill
- Quick self-test
- Output format
Layout that keeps figures honest experiments/ fig7_throughput/ run.sh # executes the sweep, writes results/*.csv with metadata header plot.py # reads results/, emits fig7.pdf — no hand-edited numbers results/ # raw outputs, never overwritten, one dir per run timestamp make fig7 # the only path by which fig7.pdf ever changes
What does the eurosys-reproducibility skill do?
Use when hardening the reproducibility story of a EuroSys paper — recording hardware and software provenance for every number, taming performance variance with repeated runs and dispersion reporting, versioning workloads and traces, and writing an availability statement that survives both double-blind review and the sysartifacts AEC.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-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.