Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Agent skill
Eurosys Reproducibility
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when hardening the reproducibility story of a EuroSys paper — recording hardware and software provenance for every number, taming performance variance with repeated runs and…
Install Eurosys Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eurosys-reproducibility --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/EuroSys-Skills/skills/eurosys-reproducibility .claude/skills/eurosys-reproducibility && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "eurosys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibility into .claude/skills/eurosys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eurosys-reproducibility", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eurosys-reproducibility --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/EuroSys-Skills/skills/eurosys-reproducibility .agents/skills/eurosys-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "eurosys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibility into .agents/skills/eurosys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eurosys-reproducibility", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eurosys-reproducibility --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/EuroSys-Skills/skills/eurosys-reproducibility .cursor/skills/eurosys-reproducibility && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "eurosys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibility into .cursor/skills/eurosys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eurosys-reproducibility", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path EuroSys-Skills/skills/eurosys-reproducibility--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eurosys-reproducibility --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/EuroSys-Skills/skills/eurosys-reproducibility .gemini/skills/eurosys-reproducibility && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "eurosys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibility into .gemini/skills/eurosys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eurosys-reproducibility", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eurosys-reproducibilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/EuroSys-Skills/skills/eurosys-reproducibility .github/skills/eurosys-reproducibility && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "eurosys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibility into .github/skills/eurosys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eurosys-reproducibility", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eurosys-reproducibility --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/EuroSys-Skills/skills/eurosys-reproducibility .opencode/skills/eurosys-reproducibility && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "eurosys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EuroSys-Skills/skills/eurosys-reproducibility into .opencode/skills/eurosys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eurosys-reproducibility", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
eurosys-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 628 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening the reproducibility story of a EuroSys paper — recording hardware and software provenance for every number, taming performance variance with repeated runs and…
- Works in 4 steps: Clean checkout, environment build from… → Regenerate two figures end to end: one… → Diff regenerated numbers against the… → …
- Hardening the reproducibility story of a EuroSys paper — recording hardware and software provenance for every number
- SKILL.md covers The provenance ledger, Variance discipline, Automation floor and Availability statement, two…, plus 4 more sections
- Calls make
What it does
Eurosys Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- 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
- Writing an availability statement that survives both double-blind review and the sysartifacts AEC
Example prompts
- “/eurosys-reproducibility”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Clean checkout, environment build from the lockfile alone — record every
- Regenerate two figures end to end: one cheap, one expensive.
- Diff regenerated numbers against the draft's numbers; investigate any
- Fix the documentation, not just the outcome — the drill's product is the
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
make
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Eurosys Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 628 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 628 words, ~1,406 tokens.
Download SKILL.mdSave it as
.claude/skills/eurosys-reproducibility/SKILL.md (or your agent's skills folder).- name
- eurosys-reproducibility
- description
- 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.
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 anecdote |
| Timestamp + raw-output path | Lets you rebuild any figure from raw logs |
Variance discipline
Single-run numbers are the most common silent reproducibility failure in systems evaluation:
- Repeat every headline measurement enough times to see its spread; report median plus an explicit dispersion measure (stdev, IQR, or min–max), and say in the caption which one it is.
- Isolate noise sources you control: pin frequencies, disable turbo where it distorts comparisons, note co-located load, randomize run order across systems so drift does not favor yours.
- Tail metrics (p99 and beyond) need far more samples than means; state the sample count whenever a tail latency is claimed.
- When a difference is within run-to-run spread, say so — EuroSys reviewers reward calibrated claims over uniform victory narratives.
Automation floor
The practical bar: any figure regenerates from raw data with one command.
# 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 changesIf a plot was ever touched manually, its provenance is broken and the AEC will find the discrepancy before you do.
Availability statement, two audiences
- Review time (double-blind): describe what exists — "an anonymized repository containing the system, workload generator, and run scripts accompanies the submission" — without leaking the lab's identity through URLs, paths, or commit authors.
- Camera-ready / AE time: replace with the DOI-backed archive and the badge set being sought. Restricted traces (production data, partner NDAs) need an honest fallback: a synthetic generator calibrated to the trace's published statistics, with the calibration method described.
Show full SKILL.md (248 more words)Show less
Restricted evidence, stated honestly
Some EuroSys evidence legitimately cannot ship — production traces under NDA, partner clusters, proprietary workloads. The honest pattern:
- Name the restriction and its scope precisely ("the ingestion trace from operator X cannot be released; its summary statistics are in Table 3").
- Ship a calibrated synthetic substitute and the calibration procedure, so external readers can approximate the regime.
- Keep at least one headline result on fully public inputs; a paper whose every number depends on unreleasable data asks reviewers for faith the venue does not trade in.
- Never let the availability paragraph imply more openness than the AEC will find; the badge process makes overstatement visible in print.
Pre-deadline reproducibility drill
One week before the paper gate, run the drill on a machine that never ran the experiments:
- Clean checkout, environment build from the lockfile alone — record every undocumented step it turns out to need.
- Regenerate two figures end to end: one cheap, one expensive.
- Diff regenerated numbers against the draft's numbers; investigate any drift beyond the reported dispersion.
- Fix the documentation, not just the outcome — the drill's product is the README the AEC will eventually read.
Quick self-test
- Can a new student regenerate Figure 7 from a clean checkout in one command?
- Does every table cell trace to a raw log file with hardware metadata?
- Are repetition counts and dispersion visible for every performance claim?
- Would the availability paragraph survive both anonymity and the AEC?
Output format
[Repro grade] regenerable / scripted-with-gaps / manual
[Ledger coverage] <numbers with full provenance / total reported numbers>
[Variance findings] <single-run claims, missing dispersion, undersampled tails>
[Workload provenance] <traces and generators with version + seed status>
[Availability draft] <review-time text and camera-ready plan>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in EuroSys-Skills/skills/eurosys-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Eurosys Reproducibility next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Eurosys Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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Compute Environment Setup
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Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
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Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
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Categories
Questions about Eurosys Reproducibility
What does Eurosys Reproducibility do?
A skill your agent uses when hardening the reproducibility story of a EuroSys paper — recording hardware and software provenance for every number, taming performance variance with repeated runs and…. Eurosys Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Eurosys Reproducibility?
Eurosys Reproducibility fits situations like: 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; writing an availability statement that survives both double-blind review and the sysartifacts AEC.
How do I install Eurosys Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a claude-code`. Or copy the skill folder (EuroSys-Skills/skills/eurosys-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/eurosys-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Eurosys Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a codex`. Or copy the skill folder (EuroSys-Skills/skills/eurosys-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/eurosys-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Eurosys Reproducibility in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eurosys-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eurosys-reproducibility, .gemini/skills/eurosys-reproducibility, .github/skills/eurosys-reproducibility and .opencode/skills/eurosys-reproducibility in your project.
What does Eurosys Reproducibility need to run?
Going by SKILL.md and its folder, Eurosys Reproducibility needs the command-line tools its instructions call (make).
Does Eurosys Reproducibility access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Eurosys Reproducibility safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Eurosys Reproducibility use?
Eurosys Reproducibility is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Eurosys Reproducibility use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Eurosys Reproducibility?
Skills that share tags, products or a category with Eurosys Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Eurosys Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.