Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Agent skill
Sigmetrics Reproducibility
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the…
Install Sigmetrics Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-reproducibility .claude/skills/sigmetrics-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 "sigmetrics-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-reproducibility into .claude/skills/sigmetrics-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-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 sigmetrics-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-reproducibility .agents/skills/sigmetrics-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 "sigmetrics-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-reproducibility into .agents/skills/sigmetrics-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 sigmetrics-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-reproducibility .cursor/skills/sigmetrics-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 "sigmetrics-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-reproducibility into .cursor/skills/sigmetrics-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 SIGMETRICS-Skills/skills/sigmetrics-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 sigmetrics-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-reproducibility .gemini/skills/sigmetrics-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 "sigmetrics-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-reproducibility into .gemini/skills/sigmetrics-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 sigmetrics-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 sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-reproducibility .github/skills/sigmetrics-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 "sigmetrics-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-reproducibility into .github/skills/sigmetrics-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 sigmetrics-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 sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-reproducibility .opencode/skills/sigmetrics-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 "sigmetrics-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-reproducibility into .opencode/skills/sigmetrics-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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
sigmetrics-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 536 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the…
- Strengthening ACM SIGMETRICS reproducibility
- SKILL.md covers Evidence map, Reproducibility-claim audit, Provenance and determinism… and Degrees of reproducibility…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering proofs and their assumptions as reproducible artifacts
What it does
Sigmetrics Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the analysis, measurement/trace provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper proves/measures and what the artifact contains.
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
- Strengthening ACM SIGMETRICS reproducibility
- Covering proofs and their assumptions as reproducible artifacts
- Seeded simulators whose figures regenerate and match the analysis
- Measurement/trace provenance
Example prompts
- “/sigmetrics-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.
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
Sigmetrics Reproducibility loads about 1.3k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 536 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). 536 words, ~1,303 tokens.
Download SKILL.mdSave it as
.claude/skills/sigmetrics-reproducibility/SKILL.md (or your agent's skills folder).- name
- sigmetrics-reproducibility
- description
- Use when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the analysis, measurement/trace provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper proves/measures and what the artifact contains.
SIGMETRICS Reproducibility
Use this before submission and again before the POMACS camera-ready. SIGMETRICS reproducibility has a distinctive shape: the "artifact" is often a proof plus a simulator plus a trace, not only running code. The goal is that a competent reader could re-derive your bound, re-run your simulation to the same curves, and re-analyze your measurement to the same conclusions.
Evidence map
- Map each theorem, bound, and reported number to a verifiable location — a proof in an appendix, a figure regenerated from a seeded simulation, or a script that turns the trace into the table.
- For analytic results, give the full derivation and every assumption; a reader should be able to check the proof and see which assumptions each step uses.
- For simulations, ship a seeded simulator whose scripts regenerate each figure and overlay the analytic prediction, so a reviewer sees model and measurement agree.
- For measurement studies, document the trace source, collection window, sanitization, and the processing scripts; archive the processed dataset or document access.
- Keep the paper and the artifact consistent: a p99 number in the PDF that no simulator run reproduces is the contradiction reviewers read as carelessness.
Reproducibility-claim audit
| Claim in the paper | Weak reproducibility answer | SIGMETRICS-ready answer |
|---|---|---|
| "Theorem 1 bounds the tail" | Proof sketch only | Full proof (appendix) + a simulation that matches the analytic curve |
| "We simulate policy X" | "Simulator available on request" | Seeded simulator + scripts that regenerate each figure from logged runs |
| "We measured system Y" | "Data on request" | Processed dataset (or documented access) + provenance + processing scripts |
| "The learner has low regret" | Empirical curve only | Regret proof + code plotting empirical regret against the bound |
"Available on request" is treated as not available; convert every such line into a concrete, anonymized artifact or an explicit, justified exception (e.g. a proprietary trace, with the methodology fully documented).
Provenance and determinism pinning
[Proof] state every assumption; give the full derivation; note which lemmas each step needs
[Simulation] log seeds; state steady-state/warm-up handling; make figures regenerate deterministically
[Measurement] pin the trace source, collection window, sanitization; archive processed data
[Compute] state hardware, runtime, and number of independent runs so a reader can size a rerun
[Agreement] ship the overlay of analysis vs. simulation so the match is reproducible, not assertedShow full SKILL.md (231 more words)Show less
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each figure/table from logged simulation runs and reproduces the analytic overlay.
- Scripted: scripts exist but require documented manual steps or access to a restricted trace.
- Descriptive: proofs and methodology detailed enough that a competent reader could rebuild the pipeline.
For SIGMETRICS, aim turnkey for anything a reviewer might rerun quickly (a simulation regenerating a figure, a script producing a table); a proprietary industrial trace may stay scripted with access documented, but the methodology and the analysis code should still be turnkey.
Vignette: a queueing-theory-plus-measurement paper
Consider a paper with a scheduling theorem and a trace-driven evaluation. Its reproducibility spine: the full proof with stated assumptions in an appendix; a seeded simulator whose notebook regenerates the analysis-vs-simulation figure; the trace-processing scripts with pinned provenance; the anonymized processed dataset (or documented access to a restricted one); and the analysis notebooks that turn logged runs into the paper's tables — plus one honest sentence about any assumption that only approximately holds and how §6 bounds it.
Consistency and camera-ready pass
- Before submission: every reported number traces to a proof, a logged simulation run, or a measurement script; the artifact is anonymized (no owner strings, cluster paths, group names).
- Before camera-ready: swap anonymized links for a permanent, DOI-issuing archive, and align the
artifact with any ACM badges you are pursuing (
sigmetrics-artifact-evaluation).
Output format
[Claim inventory] <claim -> proof / simulation run / measurement script>
[Reproducibility] concrete / vague / missing, per claim
[Provenance gaps] <proof assumptions stated? seeds logged? trace provenance pinned?>
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF/appendix>
[Artifact fixes] <additions before upload>© 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 SIGMETRICS-Skills/skills/sigmetrics-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigmetrics 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 |
|---|---|---|---|---|---|---|
| Sigmetrics 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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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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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 Sigmetrics Reproducibility
What does Sigmetrics Reproducibility do?
A skill your agent uses when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the…. Sigmetrics Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the analysis, measurement/trace provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper proves/measures and what the artifact contains.
When should I use Sigmetrics Reproducibility?
Sigmetrics Reproducibility fits situations like: strengthening ACM SIGMETRICS reproducibility; covering proofs and their assumptions as reproducible artifacts; seeded simulators whose figures regenerate and match the analysis; measurement/trace provenance.
How do I install Sigmetrics Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-reproducibility -a claude-code`. Or copy the skill folder (SIGMETRICS-Skills/skills/sigmetrics-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigmetrics-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Sigmetrics Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-reproducibility -a codex`. Or copy the skill folder (SIGMETRICS-Skills/skills/sigmetrics-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigmetrics-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Sigmetrics 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 sigmetrics-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/sigmetrics-reproducibility, .gemini/skills/sigmetrics-reproducibility, .github/skills/sigmetrics-reproducibility and .opencode/skills/sigmetrics-reproducibility in your project.
What does Sigmetrics Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigmetrics Reproducibility is instructions for the agent only.
Does Sigmetrics 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 Sigmetrics 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 Sigmetrics Reproducibility use?
Sigmetrics 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 Sigmetrics 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 Sigmetrics Reproducibility?
Skills that share tags, products or a category with Sigmetrics 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 Sigmetrics 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.