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 building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and…
Install Osdi Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill osdi-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills osdi-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/OSDI-Skills/skills/osdi-reproducibility .claude/skills/osdi-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 "osdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility into .claude/skills/osdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osdi-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/OSDI-Skills/skills/osdi-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 osdi-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills osdi-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/OSDI-Skills/skills/osdi-reproducibility .agents/skills/osdi-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 "osdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility into .agents/skills/osdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osdi-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 osdi-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills osdi-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/OSDI-Skills/skills/osdi-reproducibility .cursor/skills/osdi-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 "osdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility into .cursor/skills/osdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osdi-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 OSDI-Skills/skills/osdi-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 osdi-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills osdi-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/OSDI-Skills/skills/osdi-reproducibility .gemini/skills/osdi-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 "osdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility into .gemini/skills/osdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osdi-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 osdi-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 osdi-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/OSDI-Skills/skills/osdi-reproducibility .github/skills/osdi-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 "osdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility into .github/skills/osdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osdi-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 osdi-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 osdi-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/OSDI-Skills/skills/osdi-reproducibility .opencode/skills/osdi-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 "osdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility into .opencode/skills/osdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "osdi-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
osdi-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 774 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and…
- Building reproducibility into an OSDI systems project — recording hardware
- SKILL.md covers Why the bar is high at OSDI…, The provenance ledger, What systems papers must pin… and Determinism where it is cheap,…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Measurement provenance while experiments run
What it does
Osdi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and artifact from drifting apart, and setting up for the post-acceptance sysartifacts evaluation and open-access scrutiny.
Its SKILL.md is about 1.8k 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
- Building reproducibility into an OSDI systems project — recording hardware
- Measurement provenance while experiments run
- Keeping paper and artifact from drifting apart
- Setting up for the post-acceptance sysartifacts evaluation and open-access scrutiny
Example prompts
- “/osdi-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
No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).
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
Osdi Reproducibility loads about 1.8k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 774 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). 774 words, ~1,765 tokens.
Download SKILL.mdSave it as
.claude/skills/osdi-reproducibility/SKILL.md (or your agent's skills folder).- name
- osdi-reproducibility
- description
- Use when building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and artifact from drifting apart, and setting up for the post-acceptance sysartifacts evaluation and open-access scrutiny.
OSDI Reproducibility
Make the numbers survivable. OSDI-specific hooks below (artifact timing, badge scope, open-access exposure) are 2026-cycle facts verified 2026-07-08; the provenance discipline is venue-independent engineering.
Why the bar is high at OSDI specifically
Two venue mechanics raise the stakes beyond generic good practice:
- USENIX proceedings are open access from day one. Every reader on the internet — including the teams whose systems you outperformed — gets the free PDF immediately and can attempt your numbers. Errors get found publicly and fast.
- Artifact evaluation happens after acceptance (May 8, 2026, 8:59 pm PDT in the '26 cycle), when the experiments are months old. If provenance was not recorded while runs happened, the artifact reconstructs a memory, not an experiment.
Reproducibility at OSDI is therefore a recording problem during the project, not a
packaging problem at the end. Packaging is osdi-artifact-evaluation's job; this
skill makes packaging possible.
The provenance ledger
Maintain one machine-readable ledger, committed beside the code, updated by the run scripts themselves — never by hand after the fact:
# runs/2025-11-14-recovery-scale/ledger.yaml (written by the harness, per experiment)
experiment: recovery-vs-cluster-size # maps to RQ in the experiment matrix
commit: 4f2c9e1 (system) / 8a11d02 (harness)
hardware: 64x c6525-25g (CloudLab), 25 GbE, NVMe model+fw recorded per node
os_kernel: Ubuntu 22.04, 5.15.0-91; mitigations=on; governor=performance
baseline_versions: replayfs v2.3.1 (tag), ckptstore rebuilt from paper (SHA)
workload: trace block-2025-w2, reconstruction script + source documented
runs: 10 per point; seeds 1..10; outliers kept, plotted as distribution
raw_output: s3://bucket/runs/2025-11-14/... (checksummed)
figures: fig7 <- plot_recovery.py @ 8a11d02 on raw_outputThe last line is the anti-drift rule: every figure in the paper regenerates from checksummed raw output by a committed script. If a figure cannot name its script and input, the number it shows is unverifiable — by the AE committee and by you in June.
What systems papers must pin down
| Dimension | Must record | Common omission that kills reruns |
|---|---|---|
| Hardware | Node model, NIC, storage device + firmware, topology | The NIC/firmware detail that made the difference |
| Software | Kernel version + relevant knobs, dependency lockfile | Sysctl and IRQ-affinity settings applied by hand |
| Baselines | Exact version/tag, tuning applied, build flags | "Default settings" that were quietly edited |
| Workloads | Trace provenance, generation seed, licensing | The preprocessing script that shaped the trace |
| Measurement | Warmup policy, window, timer source, run counts | Which runs were discarded and why |
| Environment | Cluster sharing, power/turbo state, time of run | Co-located tenants distorting tail latency |
Hardware access is the honest limit of systems reproducibility: a result needing 64 specific machines will not rerun on a laptop. The discipline is disclosure plus graceful degradation — document the full testbed, and provide a scaled-down configuration that exercises every code path even if it cannot reproduce headline magnitudes.
Determinism where it is cheap, honesty where it is not
Distributed systems are not bitwise-reproducible; do not pretend otherwise. Seed what can be seeded (workload generation, placement decisions, fault-injection schedules), report distributions over repeated runs for what cannot, and state which class each reported number belongs to. A paper that says "recovery time varies ±8% run to run; we report 10-run distributions" pre-empts the reviewer who reruns and gets a different point value.
Show full SKILL.md (344 more words)Show less
Traces, data, and the licensing wall
Workload provenance is where systems reproducibility most often dies quietly:
- Production-derived traces usually cannot be redistributed. Decide the release posture before the evaluation depends on them: either obtain redistribution rights early, or build a documented reconstruction pipeline (statistical profile → generator → validation against the original) and treat the generator as part of the artifact.
- Public traces still need version pinning and checksums — public archives reorganize, and "the standard trace" is not an identifier.
- Sensitive measurements (multi-tenant clusters, user-facing services) need the anonymization step documented as code, because it changes distributions and a reproducer must know how.
- Whatever the posture, the paper states it in one honest sentence; discovering an unreleasable dataset during artifact evaluation reads far worse than declaring it in the submission.
The shared smoke checker in
../../resources/code/README.md catches structural
gaps (missing README/manifest/license) but none of the above — licensing and
provenance are judgment calls only the authors can make.
Timing against the 2026 cycle
- During experiments (autumn) — the ledger above, enforced by the harness.
- Silent review window (Dec–Mar) — freeze the testbed image, trace archives, and
environment; the '26 Call for Artifacts encouraged preparing artifacts while the
paper was under consideration, and a conditional accept may demand new runs on the
frozen setup (
osdi-author-response). - After notification (Mar 26) — packaging sprint to the May 8 artifact deadline;
in 2026 the badge evaluated was Artifacts Available, so permanent archiving is
the floor — but a ledger-backed artifact is what makes the optional two-page
Artifact Appendix in the final paper worth writing (
osdi-camera-ready).
The handoff test
The standing acceptance test for all of the above: a new group member, given only the repository and the ledger, regenerates one paper figure on the scaled-down configuration without asking anyone anything. Run it quarterly and before each gate (submission, artifact deadline, final paper). Every question they are forced to ask is a missing ledger entry; every mismatch they hit is drift between paper and artifact that an AE evaluator — or a public reproducer holding the open-access PDF — would have found later, with an audience.
Output format
[Ledger] exists + harness-written? gaps: <dimensions from the table>
[Figure regeneration] all figures script+input traceable? failures: <list>
[Determinism statement] seeded vs distributional numbers classified? yes/no
[Testbed freeze] image/trace archive frozen for the review window? yes/no
[AE readiness] distance from ledger to packageable artifact: <low/med/high>© 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 OSDI-Skills/skills/osdi-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Osdi 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 |
|---|---|---|---|---|---|---|
| Osdi Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | 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 |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
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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Questions about Osdi Reproducibility
What does Osdi Reproducibility do?
A skill your agent uses when building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and…. Osdi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and artifact from drifting apart, and setting up for the post-acceptance sysartifacts evaluation and open-access scrutiny.
When should I use Osdi Reproducibility?
Osdi Reproducibility fits situations like: building reproducibility into an OSDI systems project — recording hardware; measurement provenance while experiments run; keeping paper and artifact from drifting apart; setting up for the post-acceptance sysartifacts evaluation and open-access scrutiny.
How do I install Osdi Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill osdi-reproducibility -a claude-code`. Or copy the skill folder (OSDI-Skills/skills/osdi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/osdi-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Osdi Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill osdi-reproducibility -a codex`. Or copy the skill folder (OSDI-Skills/skills/osdi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/osdi-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Osdi 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 osdi-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/osdi-reproducibility, .gemini/skills/osdi-reproducibility, .github/skills/osdi-reproducibility and .opencode/skills/osdi-reproducibility in your project.
What does Osdi Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Osdi Reproducibility is instructions for the agent only.
Does Osdi 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 Osdi 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 Osdi Reproducibility use?
Osdi 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 Osdi Reproducibility use?
About 1.8k tokens (SKILL.md is roughly 7.1k 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 Osdi Reproducibility?
Skills that share tags, products or a category with Osdi 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 Osdi 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.