Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Agent skill
Sigmod Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when preparing a SIGMOD paper's code and data for the Availability & Reproducibility Initiative (ARI), covering the post-acceptance opt-in, HotCRP artifact registration, the…
Install Sigmod Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-artifact-evaluation --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/SIGMOD-Skills/skills/sigmod-artifact-evaluation .claude/skills/sigmod-artifact-evaluation && 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 "sigmod-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-artifact-evaluation into .claude/skills/sigmod-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-artifact-evaluation", 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/SIGMOD-Skills/skills/sigmod-artifact-evaluationType 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 sigmod-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-artifact-evaluation --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/SIGMOD-Skills/skills/sigmod-artifact-evaluation .agents/skills/sigmod-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sigmod-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-artifact-evaluation into .agents/skills/sigmod-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-artifact-evaluation", 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 sigmod-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-artifact-evaluation --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/SIGMOD-Skills/skills/sigmod-artifact-evaluation .cursor/skills/sigmod-artifact-evaluation && 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 "sigmod-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-artifact-evaluation into .cursor/skills/sigmod-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-artifact-evaluation", 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 SIGMOD-Skills/skills/sigmod-artifact-evaluation--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 sigmod-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-artifact-evaluation --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/SIGMOD-Skills/skills/sigmod-artifact-evaluation .gemini/skills/sigmod-artifact-evaluation && 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 "sigmod-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-artifact-evaluation into .gemini/skills/sigmod-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-artifact-evaluation", 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 sigmod-artifact-evaluationInstalls 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 sigmod-artifact-evaluation -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/SIGMOD-Skills/skills/sigmod-artifact-evaluation .github/skills/sigmod-artifact-evaluation && 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 "sigmod-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-artifact-evaluation into .github/skills/sigmod-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-artifact-evaluation", 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 sigmod-artifact-evaluation -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 sigmod-artifact-evaluation --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/SIGMOD-Skills/skills/sigmod-artifact-evaluation .opencode/skills/sigmod-artifact-evaluation && 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 "sigmod-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-artifact-evaluation into .opencode/skills/sigmod-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-artifact-evaluation", 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
sigmod-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 651 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when preparing a SIGMOD paper's code and data for the Availability & Reproducibility Initiative (ARI), covering the post-acceptance opt-in, HotCRP artifact registration, the…
- Works in 4 steps: During review: keep the anonymized… → At camera-ready: publish and tag the… → At ARI registration: submit the tagged… → …
- Preparing a SIGMOD papers code and data for the Availability & Reproducibility Initiative (ARI)
- SKILL.md covers The badge ladder, What ARI evaluators grade, Hardware honesty and Packaging pattern that passes, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Sigmod Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing a SIGMOD paper's code and data for the Availability & Reproducibility Initiative (ARI), covering the post-acceptance opt-in, HotCRP artifact registration, the Artifacts Available / Artifacts Evaluated / Results Reproduced badges, evaluator criteria, and the Best Artifact award path.
Its SKILL.md is about 1.5k 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
- Preparing a SIGMOD papers code and data for the Availability & Reproducibility Initiative (ARI)
- Covering the post-acceptance opt-in
- HotCRP artifact registration
- The Artifacts Available / Artifacts Evaluated / Results Reproduced badges
Example prompts
- “/sigmod-artifact-evaluation”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- During review: keep the anonymized package coherent (see
- At camera-ready: publish and tag the release matching published numbers
- At ARI registration: submit the tagged version via the edition's HotCRP,
- After badging: keep the archive stable; the badge points at the DL
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
Sigmod Artifact Evaluation loads about 1.5k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 651 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). 651 words, ~1,474 tokens.
Download SKILL.mdSave it as
.claude/skills/sigmod-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- sigmod-artifact-evaluation
- description
- Use when preparing a SIGMOD paper's code and data for the Availability & Reproducibility Initiative (ARI), covering the post-acceptance opt-in, HotCRP artifact registration, the Artifacts Available / Artifacts Evaluated / Results Reproduced badges, evaluator criteria, and the Best Artifact award path.
SIGMOD Artifact Evaluation
SIGMOD runs a named, long-standing artifact program: the Availability &
Reproducibility Initiative (ARI) at reproducibility.sigmod.org. It is
optional, happens after acceptance, and is decoupled from the accept/
reject decision — but its badges are embedded into the paper's PDF in the ACM
Digital Library, so the payoff is permanent and public. Recent editions
registered artifacts through a dedicated HotCRP site (e.g.,
sigmod25ari.hotcrp.com); confirm the current edition's site and dates
before promising a timeline.
The badge ladder
| Badge | What evaluators must confirm | Typical blocker |
|---|---|---|
| Artifacts Available | Code, data, scripts, notebooks reachable at a stable public location | Link rot; "email us for the dataset" |
| Artifacts Evaluated | Package is exercisable and well documented | Undocumented cluster assumptions |
| Results Reproduced | Key results of the paper independently regenerated | Figures that need hand-tuned steps |
Aim explicitly at one rung. A package engineered for Results Reproduced looks different from one that merely publishes source: it names which figures and tables constitute the "key results" and drives each one end to end.
What ARI evaluators grade
The initiative's stated criteria are coverage (how much of the paper the artifact backs), ease of reproducibility (how little effort a rerun takes), flexibility (can parameters, workloads, and datasets be varied), and portability (does it run beyond the authors' exact machine). Database artifacts fail these in predictable ways:
- Coverage: the artifact rebuilds microbenchmarks but not the headline end-to-end comparison against the competing engine.
- Ease: a 40-step README where a driver script should be.
- Flexibility: scale factors and thread counts hard-coded into binaries.
- Portability: kernel-version, NUMA-layout, or GPU assumptions that only hold on the lab machine; no container or VM escape hatch.
Hardware honesty
Data-management experiments often need big machines. ARI evaluations have historically accommodated this via cloud credits or author-provided access in some editions — but the current edition's policy must be checked, not assumed. Regardless of policy:
- State minimum and recommended hardware in the README's first screen.
- Provide a reduced-scale mode (smaller scale factor, fewer threads) that preserves every qualitative trend, and say which absolute numbers will differ from the paper.
- Wall-clock estimates per experiment; evaluators budget time, and an unannounced 30-hour run is how evaluations stall.
Packaging pattern that passes
artifact/
README.md # claims map: Fig/Table -> script -> expected output
LICENSE
Dockerfile # or VM image link; pinned OS + dependency versions
data/fetch.sh # pulls public datasets by checksum; sizes stated
run_all.sh # full reproduction, prints per-step ETA
run_small.sh # laptop-scale variant, trends preserved
experiments/
fig7_throughput/ # one directory per paper artifact, self-contained
tab3_latency/
plot/ # regenerates the exact PDF figures from raw logsThe claims map is the single highest-leverage file: a table from paper artifact to command to expected output, with tolerances ("within 10% on different hardware; ordering of systems preserved").
Show full SKILL.md (263 more words)Show less
Incentives beyond the badge
SIGMOD confers a Best Artifact award through the initiative, and reproducibility reports from past editions are themselves published in the ACM DL — meaning strong artifacts earn citable recognition. For an engine or index paper, the artifact also becomes the de facto baseline implementation future papers must compare against, which compounds citations for years.
Questions evaluators ask that READMEs rarely answer
- Which exact figure numbers count as the paper's key results, and where is that stated?
- What should I see on screen when a run succeeds — and what does a known benign warning look like, so I don't abort a healthy run?
- How much disk does the full dataset expand to after decompression?
- Can steps be resumed after a failure, or does every retry start from data fetch?
- Which numbers are hardware-sensitive, and how much drift is acceptable before I should suspect a real problem?
- Who do I contact (anonymity no longer applies) if a step fails, and how fast do authors respond during the evaluation window?
Answer all six in the README's first two screens and the evaluation's most common failure mode — silent stall and abandonment — mostly disappears.
Sequencing with the paper lifecycle
- During review: keep the anonymized package coherent (see
sigmod-supplementary); it becomes the ARI seed. - At camera-ready: publish and tag the release matching published numbers
(see
sigmod-camera-ready). - At ARI registration: submit the tagged version via the edition's HotCRP, then leave it frozen — evaluate-time pushes create version skew.
- After badging: keep the archive stable; the badge points at the DL record forever.
Output format
[Target badge] Available / Evaluated / Results Reproduced
[Claims map] Fig/Table -> script coverage, gaps listed
[Criteria audit] coverage / ease / flexibility / portability findings
[Hardware plan] full-scale needs, reduced-scale mode, runtimes
[Registration] ARI edition site + dates confirmed or 待核实
[Fix queue] ordered work before artifact submission© 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 SIGMOD-Skills/skills/sigmod-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigmod Artifact Evaluation 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 |
|---|---|---|---|---|---|---|
| Sigmod Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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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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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Categories
Questions about Sigmod Artifact Evaluation
What does Sigmod Artifact Evaluation do?
A skill your agent uses when preparing a SIGMOD paper's code and data for the Availability & Reproducibility Initiative (ARI), covering the post-acceptance opt-in, HotCRP artifact registration, the…. Sigmod Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing a SIGMOD paper's code and data for the Availability & Reproducibility Initiative (ARI), covering the post-acceptance opt-in, HotCRP artifact registration, the Artifacts Available / Artifacts Evaluated / Results Reproduced badges, evaluator criteria, and the Best Artifact award path.
When should I use Sigmod Artifact Evaluation?
Sigmod Artifact Evaluation fits situations like: preparing a SIGMOD papers code and data for the Availability & Reproducibility Initiative (ARI); covering the post-acceptance opt-in; hotCRP artifact registration; the Artifacts Available / Artifacts Evaluated / Results Reproduced badges.
How do I install Sigmod Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-artifact-evaluation -a claude-code`. Or copy the skill folder (SIGMOD-Skills/skills/sigmod-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigmod-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Sigmod Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-artifact-evaluation -a codex`. Or copy the skill folder (SIGMOD-Skills/skills/sigmod-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigmod-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Sigmod Artifact Evaluation 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 sigmod-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sigmod-artifact-evaluation, .gemini/skills/sigmod-artifact-evaluation, .github/skills/sigmod-artifact-evaluation and .opencode/skills/sigmod-artifact-evaluation in your project.
What does Sigmod Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigmod Artifact Evaluation is instructions for the agent only.
Does Sigmod Artifact Evaluation 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 Sigmod Artifact Evaluation 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 Sigmod Artifact Evaluation use?
Sigmod Artifact Evaluation 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 Sigmod Artifact Evaluation use?
About 1.5k tokens (SKILL.md is roughly 5.9k 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 Sigmod Artifact Evaluation?
Skills that share tags, products or a category with Sigmod Artifact Evaluation: 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 Sigmod Artifact Evaluation?
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.