eurosys-experiments
Use when designing or auditing the evaluation of a EuroSys paper — choosing workloads that earn the word realistic, tuning baselines beyond their defaults, decomposing end-to-end wins into per-mechanism gains, measuring overheads and worst cases, and sizing experiments to the claims the paper actually makes.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-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.
# EuroSys Experiments Use this while the evaluation is still designable. EuroSys reviewers treat the evaluation as the paper's testimony about itself: the design section says what should happen, and §Evaluation is cross-examination. Plan it as a set of questions with falsifiable answers, not as a benchmark tour. ## The four-layer evidence stack | Layer | Question it answers | Typical EuroSys instrument | |---|---|---| | End-to-end | Does the system win where users live? | Full application or serving workload, realistic scale | | Decomposition | Which mechanism buys which fraction? | Factor analysis: enable components one at a time | | Cost | What does the win spend? | Memory/CPU/network overhead, code and ops complexity | | Boundary | Where does it stop winning? | Adversarial mixes, saturation, failure injection | A submission with only the first layer reads as a demo; the middle layers are what convert "it is faster" into "we understand why it is faster", which is the systems-research standard of proof. ## Workload realism, argued not asserted - Prefer published traces and standard suites with named versions; when using a synthetic generator, calibrate it against a real distributi
- The four-layer evidence stack
- Workload realism, argued not asserted
- Baseline fairness — the venue's sharpest knife
- Experiment matrix as an artifact
- Result invalidators to design out early
- Sizing runs for the claim
- Reporting floor for the paper
- Output format
What does the eurosys-experiments skill do?
Use when designing or auditing the evaluation of a EuroSys paper — choosing workloads that earn the word realistic, tuning baselines beyond their defaults, decomposing end-to-end wins into per-mechanism gains, measuring overheads and worst cases, and sizing experiments to the claims the paper actually makes.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-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 984 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.