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 making an NSDI paper's results reconstructible — capturing testbed topology, trace provenance, and configuration while experiments run, planning which datasets can ship…
Install Nsdi Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill nsdi-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills nsdi-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/NSDI-Skills/skills/nsdi-reproducibility .claude/skills/nsdi-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 "nsdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NSDI-Skills/skills/nsdi-reproducibility into .claude/skills/nsdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nsdi-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/NSDI-Skills/skills/nsdi-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 nsdi-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills nsdi-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/NSDI-Skills/skills/nsdi-reproducibility .agents/skills/nsdi-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 "nsdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NSDI-Skills/skills/nsdi-reproducibility into .agents/skills/nsdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nsdi-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 nsdi-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills nsdi-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/NSDI-Skills/skills/nsdi-reproducibility .cursor/skills/nsdi-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 "nsdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NSDI-Skills/skills/nsdi-reproducibility into .cursor/skills/nsdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nsdi-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 NSDI-Skills/skills/nsdi-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 nsdi-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills nsdi-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/NSDI-Skills/skills/nsdi-reproducibility .gemini/skills/nsdi-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 "nsdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NSDI-Skills/skills/nsdi-reproducibility into .gemini/skills/nsdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nsdi-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 nsdi-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 nsdi-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/NSDI-Skills/skills/nsdi-reproducibility .github/skills/nsdi-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 "nsdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NSDI-Skills/skills/nsdi-reproducibility into .github/skills/nsdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nsdi-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 nsdi-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 nsdi-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/NSDI-Skills/skills/nsdi-reproducibility .opencode/skills/nsdi-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 "nsdi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NSDI-Skills/skills/nsdi-reproducibility into .opencode/skills/nsdi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nsdi-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
nsdi-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 692 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when making an NSDI paper's results reconstructible — capturing testbed topology, trace provenance, and configuration while experiments run, planning which datasets can ship…
- Works in 3 steps: Public now — code, synthetic generators,… → Public after transformation —… → Never public — proprietary traces,…
- Making an NSDI papers results reconstructible — capturing testbed topology
- SKILL.md covers The four provenance ledgers, Determinism is different here, What can actually ship? and Wiring it into the repo, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Nsdi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making an NSDI paper's results reconstructible — capturing testbed topology, trace provenance, and configuration while experiments run, planning which datasets can ship publicly, and keeping the paper and artifact from drifting apart so badge evaluation and the Community Award stay reachable.
Its SKILL.md is about 1.6k 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
- Making an NSDI papers results reconstructible — capturing testbed topology
- Trace provenance
- Configuration while experiments run
- Planning which datasets can ship publicly
Example prompts
- “/nsdi-reproducibility”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Public now — code, synthetic generators, testbed configs, analysis scripts.
- Public after transformation — production-derived traces that can be
- Never public — proprietary traces, customer data. Plan the fallback: a
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 bash).
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
Nsdi Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 692 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). 692 words, ~1,592 tokens.
Download SKILL.mdSave it as
.claude/skills/nsdi-reproducibility/SKILL.md (or your agent's skills folder).- name
- nsdi-reproducibility
- description
- Use when making an NSDI paper's results reconstructible — capturing testbed topology, trace provenance, and configuration while experiments run, planning which datasets can ship publicly, and keeping the paper and artifact from drifting apart so badge evaluation and the Community Award stay reachable.
NSDI Reproducibility
A networked-systems result is a function of topology, traffic, timing, and code — and three of those four are absent from the PDF unless deliberately recorded. NSDI rewards the discipline institutionally: artifact badges after acceptance and a Community Award for the best paper whose code and/or dataset is public by the final-papers deadline. But the work happens during the experiments, not after the decision email.
The four provenance ledgers
Keep each as a versioned file next to the results it explains:
| Ledger | Contents | Loss mode it prevents |
|---|---|---|
| Topology | node specs, NIC/switch models, link speeds, RTT matrix, kernel + NIC settings | "worked on our cluster," unreproducible knee points |
| Traffic | trace source + collection context, scaling/anonymization transforms, synthetic-generator parameters + seeds | headline numbers tied to data nobody can regenerate |
| Configuration | every config diff from defaults, per system and per baseline | untuned-baseline accusations you cannot rebut |
| Run | per-experiment: commit hash, config snapshot, fault schedule, raw-log location, analysis-script hash | figures that cannot be regenerated at revision time |
The run ledger has a second life NSDI makes unusually likely: a one-shot revision letter can demand new measurements on the same setup, 3-4 months after submission, at a subsequent deadline. Teams with ledgers rerun in days; teams without them rebuild the testbed from memory.
Determinism is different here
Unlike ML reproducibility, networked-systems results are legitimately non-deterministic — background load, timer jitter, and cross-traffic vary. The honest posture is not "identical numbers" but characterized variance:
- Fix what can be fixed: seeds for generators and fault schedules, pinned software versions, isolated or documented-shared testbeds.
- Measure what cannot: repeat headline experiments across days/trace slices; publish the spread with the artifact so an evaluator knows a 7% delta is noise, not failure.
- Timestamp everything; time-of-day and neighbor effects are real explanations, but only if recorded.
What can actually ship?
Decide per data item before the camera-ready crunch, because the answer shapes the paper's claims:
- Public now — code, synthetic generators, testbed configs, analysis scripts. Default to shipping.
- Public after transformation — production-derived traces that can be anonymized/aggregated. Document the transform and what properties it preserves; state in the paper that released traces differ from raw ones.
- Never public — proprietary traces, customer data. Plan the fallback: a synthetic workload with matched characteristics, shipped alongside a fidelity note. Do not let a never-public trace be the only support for a headline claim if a shippable proxy can corroborate it.
Anonymize the artifact itself for review-time supplements (repo owners, hostnames, paths, company strings in configs); de-anonymize only at final-paper time.
Show full SKILL.md (277 more words)Show less
Wiring it into the repo
# Layout that keeps paper and artifact from drifting
experiments/
<exp-id>/run.sh # topology + config + fault schedule, self-describing
<exp-id>/provenance.json # commit, trace id, seeds, dates, operator
figures/
Makefile # every paper figure regenerated from raw logs:
# make fig6 -> pulls exp logs, runs analysis, emits PDF
paper/
claims.md # claim -> exp-id -> figure mapping, reviewed at freezeThe claims.md cross-map is the cheapest anti-drift device: at submission freeze,
walk it once; any claim whose exp-id is stale gets rerun or reworded. The shared
smoke-checker
(../../resources/code/README.md) covers package
hygiene but not topology/trace fidelity — those checks are yours.
Blind now, open later
Reproducibility material crosses the double-blind boundary twice, and each crossing has a checklist:
- At submission (if any package is uploaded as auxiliary material): scrub repository owners, commit-author emails, hostnames that embed the institution, and license files naming the organization; keep the scrubbed and true versions as separate branches so de-anonymization is a merge, not a rewrite.
- At final papers: flip to the public, attributed release — DOI'd archive,
real repository, institutional acknowledgment — dated before the final-papers
deadline if the Community Award matters (
nsdi-camera-ready).
Cheap wins ordered by payoff
provenance.jsonper experiment, written by the runner script (an hour to automate).- Figure Makefile with raw-log inputs (removes an entire class of revision pain).
- Baseline config diffs recorded (rebuts the most common reviewer attack).
- Variance characterization for the two headline results.
- Early legal check on trace release — licensing takes longer than deadline gaps.
Signs the discipline is slipping
- A figure in the draft that nobody can regenerate this week.
- "The good run" language — selecting runs by outcome rather than by protocol.
- Baseline numbers copied forward from an earlier paper draft instead of re-measured on the current topology.
- Testbed changes (kernel upgrade, NIC swap) mid-campaign without a ledger entry splitting before/after results.
Any one of these, found at submission freeze, costs days; found by an artifact evaluator or a revision reviewer, it costs the result's credibility.
Output format
[Ledger status] topology / traffic / configuration / run — each present/partial/absent
[Variance] headline results with characterized spread? which lack repeats?
[Shippability] data items -> public / transformable / never (fallback named)
[Drift check] claims.md walked? stale claim list
[Award posture] on track for public code+data by final-papers deadline?
[Next actions] ordered by payoff-per-hour© 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 NSDI-Skills/skills/nsdi-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Nsdi 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 |
|---|---|---|---|---|---|---|
| Nsdi Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | 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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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
yushui2022/MathModel-Skill
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 Nsdi Reproducibility
What does Nsdi Reproducibility do?
A skill your agent uses when making an NSDI paper's results reconstructible — capturing testbed topology, trace provenance, and configuration while experiments run, planning which datasets can ship…. Nsdi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making an NSDI paper's results reconstructible — capturing testbed topology, trace provenance, and configuration while experiments run, planning which datasets can ship publicly, and keeping the paper and artifact from drifting apart so badge evaluation and the Community Award stay reachable.
When should I use Nsdi Reproducibility?
Nsdi Reproducibility fits situations like: making an NSDI papers results reconstructible — capturing testbed topology; trace provenance; configuration while experiments run; planning which datasets can ship publicly.
How do I install Nsdi Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill nsdi-reproducibility -a claude-code`. Or copy the skill folder (NSDI-Skills/skills/nsdi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/nsdi-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Nsdi Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill nsdi-reproducibility -a codex`. Or copy the skill folder (NSDI-Skills/skills/nsdi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/nsdi-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Nsdi 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 nsdi-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/nsdi-reproducibility, .gemini/skills/nsdi-reproducibility, .github/skills/nsdi-reproducibility and .opencode/skills/nsdi-reproducibility in your project.
What does Nsdi Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Nsdi Reproducibility is instructions for the agent only.
Does Nsdi 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 Nsdi 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 Nsdi Reproducibility use?
Nsdi 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 Nsdi Reproducibility use?
About 1.6k tokens (SKILL.md is roughly 6.4k 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 Nsdi Reproducibility?
Skills that share tags, products or a category with Nsdi 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 Nsdi 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.