Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when hardening the reproducibility of a SOSP paper's results before submission — pinning the OS-level environment, recording hardware and topology, making every figure…
Install Sosp Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sosp-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sosp-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/SOSP-Skills/skills/sosp-reproducibility .claude/skills/sosp-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 "sosp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SOSP-Skills/skills/sosp-reproducibility into .claude/skills/sosp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sosp-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/SOSP-Skills/skills/sosp-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 sosp-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sosp-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/SOSP-Skills/skills/sosp-reproducibility .agents/skills/sosp-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 "sosp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SOSP-Skills/skills/sosp-reproducibility into .agents/skills/sosp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sosp-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 sosp-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sosp-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/SOSP-Skills/skills/sosp-reproducibility .cursor/skills/sosp-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 "sosp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SOSP-Skills/skills/sosp-reproducibility into .cursor/skills/sosp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sosp-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 SOSP-Skills/skills/sosp-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 sosp-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sosp-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/SOSP-Skills/skills/sosp-reproducibility .gemini/skills/sosp-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 "sosp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SOSP-Skills/skills/sosp-reproducibility into .gemini/skills/sosp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sosp-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 sosp-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 sosp-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/SOSP-Skills/skills/sosp-reproducibility .github/skills/sosp-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 "sosp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SOSP-Skills/skills/sosp-reproducibility into .github/skills/sosp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sosp-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 sosp-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 sosp-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/SOSP-Skills/skills/sosp-reproducibility .opencode/skills/sosp-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 "sosp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SOSP-Skills/skills/sosp-reproducibility into .opencode/skills/sosp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sosp-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
sosp-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 560 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 of a SOSP paper's results before submission — pinning the OS-level environment, recording hardware and topology, making every figure…
- Hardening the reproducibility of a SOSP papers results before submission — pinning the OS-level environment
- SKILL.md covers The OS-research twist: your…, Capture the environment…, One command per figure and Variance is a first-class result, plus 2 more sections
- Calls git
- Recording hardware and topology
What it does
Sosp Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility of a SOSP paper's results before submission — pinning the OS-level environment, recording hardware and topology, making every figure regenerable from logged runs, separating measurement noise from effect size, and preparing the ground for post-acceptance artifact evaluation.
Its SKILL.md is about 1.3k 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 of a SOSP papers results before submission — pinning the OS-level environment
- Recording hardware and topology
- Making every figure regenerable from logged runs
- Separating measurement noise from effect size
Example prompts
- “/sosp-reproducibility”
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:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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
Sosp Reproducibility loads about 1.3k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 560 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). 560 words, ~1,304 tokens.
Download SKILL.mdSave it as
.claude/skills/sosp-reproducibility/SKILL.md (or your agent's skills folder).- name
- sosp-reproducibility
- description
- Use when hardening the reproducibility of a SOSP paper's results before submission — pinning the OS-level environment, recording hardware and topology, making every figure regenerable from logged runs, separating measurement noise from effect size, and preparing the ground for post-acceptance artifact evaluation.
SOSP Reproducibility
Use this while experiments are being designed and run — months before the SOSP
deadline, not during artifact evaluation. At SOSP the artifact process happens after
acceptance (see sosp-artifact-evaluation), which means reproducibility discipline has
a different job pre-submission: it protects you. Reviewers probe numbers during a
three-month review cycle and a response phase in which new experiments are forbidden;
the only defensible paper is one whose every number can be traced to a logged,
re-runnable measurement of a pinned system.
The OS-research twist: your system changes the platform
An operating-systems artifact often is the environment — a modified kernel, a new scheduler, an interposed I/O path. That collapses the usual app/platform separation and creates specific hazards:
- A rebased kernel patch series can silently change baseline behavior; record the exact base commit and the full patch stack for every run, including baseline runs.
- Firmware, microcode, and mitigations (for example, speculative-execution mitigations) can dominate syscall-heavy microbenchmarks; log them and hold them constant across system and baseline.
- Frequency scaling, turbo states, and NUMA placement move tail latencies by tens of percent; fix the policy, and record it rather than assuming defaults.
Capture the environment mechanically
Hand-written "Experimental Setup" sections drift from reality. Generate the facts:
#!/usr/bin/env bash
# capture-env.sh — run on every experiment node, archive with the run's results
{
uname -a; cat /etc/os-release | head -2
cat /proc/cmdline # mitigations, isolcpus, hugepages
lscpu | grep -E 'Model name|Socket|NUMA|MHz'
free -h | head -2; lsblk -d -o NAME,MODEL,ROTA,SIZE
ip -br link; ethtool eth0 2>/dev/null | grep -E 'Speed|Duplex'
git -C "$REPO" rev-parse HEAD; git -C "$REPO" status --porcelain
} > "env-$(hostname)-$(date +%Y%m%dT%H%M%S).txt"Archive one such file per node per experiment batch, next to the raw results. When a reviewer asks eleven weeks later whether the baseline ran with the same mitigations, the answer is a file, not a memory.
One command per figure
The standard that survives review pressure: every figure and table in the paper is produced by a script that reads only archived raw logs. No spreadsheet steps, no hand-transcribed numbers. This is also what makes the response phase survivable — you can re-check any reviewer-doubted number against raw data without re-running anything, which is the only kind of "checking" the response rules allow.
| Discipline | Pre-submission payoff | Post-acceptance payoff |
|---|---|---|
| Raw logs archived per run, immutable | Response-phase answers under the no-new-data rule | AE claims map writes itself |
| Figure scripts read logs only | No PDF/data divergence between drafts | Evaluators regenerate your plots |
| Environment capture per node per batch | Detects config drift between system and baseline runs | hardware/ directory is done |
| Run manifest (who, when, which commit, which config) | Attribution when a number looks off | Provenance for the archival artifact |
Show full SKILL.md (182 more words)Show less
Variance is a first-class result
Systems effects live in distributions. Reproducibility at SOSP includes making the noise floor explicit:
- Report repetitions and the spread (percentiles or CIs), especially for tail-latency claims — a p99 from one run of one trial is folklore, not a measurement.
- Distinguish sources: run-to-run jitter, node-to-node hardware variation, and time-of-day effects on shared testbeds. If experiments ran on a shared cluster, say so and quantify what that cost in variance.
- Decide the warm-up policy (discard first N iterations? cold-start included?) once, document it, and apply it uniformly; asymmetric warm-up between system and baseline is a classic self-inflicted review wound.
Traces and workloads you cannot publish
Production traces make SOSP evaluations compelling and reproductions hard. The honest pattern: characterize the private trace in the paper (size, arrival statistics, skew, whatever drives the result), release a synthetic generator matched to those statistics, and show at least one headline experiment where synthetic and real traces agree in trend. Flag in the paper which results are re-runnable and which are documented-only — the same tiering the artifact evaluation will ask for later.
Output format
[Repro posture] traceable / partially / folklore
[Environment] capture automated? mitigations+kernel pinned for baseline too?
[Figure pipeline] all one-command? exceptions: <list>
[Variance] repetitions, spread reported, warm-up policy uniform?
[Private data] trace characterization + generator plan
[Gap list] <ordered fixes before the freeze>© 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 SOSP-Skills/skills/sosp-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sosp 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 |
|---|---|---|---|---|---|---|
| Sosp Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | 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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xjtulyc/MedgeClaw
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Compute Environment Setup
aipoch/open-science
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
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
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Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Sosp Reproducibility
What does Sosp Reproducibility do?
A skill your agent uses when hardening the reproducibility of a SOSP paper's results before submission — pinning the OS-level environment, recording hardware and topology, making every figure…. Sosp Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility of a SOSP paper's results before submission — pinning the OS-level environment, recording hardware and topology, making every figure regenerable from logged runs, separating measurement noise from effect size, and preparing the ground for post-acceptance artifact evaluation.
When should I use Sosp Reproducibility?
Sosp Reproducibility fits situations like: hardening the reproducibility of a SOSP papers results before submission — pinning the OS-level environment; recording hardware and topology; making every figure regenerable from logged runs; separating measurement noise from effect size.
How do I install Sosp Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sosp-reproducibility -a claude-code`. Or copy the skill folder (SOSP-Skills/skills/sosp-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sosp-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Sosp Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sosp-reproducibility -a codex`. Or copy the skill folder (SOSP-Skills/skills/sosp-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sosp-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Sosp 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 sosp-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/sosp-reproducibility, .gemini/skills/sosp-reproducibility, .github/skills/sosp-reproducibility and .opencode/skills/sosp-reproducibility in your project.
What does Sosp Reproducibility need to run?
Going by SKILL.md and its folder, Sosp Reproducibility needs the command-line tools its instructions call (git).
Does Sosp Reproducibility access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Sosp 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 Sosp Reproducibility use?
Sosp 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 Sosp Reproducibility use?
About 1.3k tokens (SKILL.md is roughly 5.2k 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 Sosp Reproducibility?
Skills that share tags, products or a category with Sosp 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 Sosp 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.