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 the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement…
Install Ase Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ase-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/ASE-Skills/skills/ase-reproducibility .claude/skills/ase-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 "ase-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASE-Skills/skills/ase-reproducibility into .claude/skills/ase-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ase-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/ASE-Skills/skills/ase-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 ase-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ase-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/ASE-Skills/skills/ase-reproducibility .agents/skills/ase-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 "ase-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASE-Skills/skills/ase-reproducibility into .agents/skills/ase-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ase-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 ase-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ase-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/ASE-Skills/skills/ase-reproducibility .cursor/skills/ase-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 "ase-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASE-Skills/skills/ase-reproducibility into .cursor/skills/ase-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ase-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 ASE-Skills/skills/ase-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 ase-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ase-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/ASE-Skills/skills/ase-reproducibility .gemini/skills/ase-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 "ase-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASE-Skills/skills/ase-reproducibility into .gemini/skills/ase-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ase-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 ase-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 ase-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/ASE-Skills/skills/ase-reproducibility .github/skills/ase-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 "ase-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASE-Skills/skills/ase-reproducibility into .github/skills/ase-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ase-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 ase-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 ase-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/ASE-Skills/skills/ase-reproducibility .opencode/skills/ase-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 "ase-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASE-Skills/skills/ase-reproducibility into .opencode/skills/ase-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ase-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
ase-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 456 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement…
- Building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission
- SKILL.md covers The mandatory Data…, Anonymized-but-runnable tools, Provenance pinning (do this at… and Reproducibility failure modes…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the mandatory Data Availability Statement
What it does
Ase Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement, anonymized-but-runnable tools, tool and subject-system provenance pinning, cached LLM outputs, and staging for the ACM Available/Reusable artifact badges.
Its SKILL.md is about 1.1k 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 the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission
- Covering the mandatory Data Availability Statement
- Anonymized-but-runnable tools
- Tool and subject-system provenance pinning
Example prompts
- “/ase-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
Ase Reproducibility loads about 1.1k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 456 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). 456 words, ~1,073 tokens.
Download SKILL.mdSave it as
.claude/skills/ase-reproducibility/SKILL.md (or your agent's skills folder).- name
- ase-reproducibility
- description
- Use when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement, anonymized-but-runnable tools, tool and subject-system provenance pinning, cached LLM outputs, and staging for the ACM Available/Reusable artifact badges.
ASE Reproducibility
Build the reproducibility story at data-collection time, not at submission. ASE requires a mandatory Data Availability Statement in the paper and expects an anonymized, runnable artifact at review time; automated-SE artifacts are usually tools, so "runnable" means a reviewer can actually execute the automation on stated subjects. What is not pinned when you collect it cannot be reconstructed later.
The mandatory Data Availability Statement
- Required, placed after the Conclusions and inside the 10-page limit (it is not free appendix space).
- State what exists — the tool, the dataset, the subject systems, the scripts, the logs — and where it will live after acceptance (an archival DOI target).
- Provide an anonymized link or upload now; "available upon request" reads as a scored weakness, not a neutral placeholder.
- Match the statement to what the archive actually contains — an overclaiming statement is worse than a modest, honest one.
Anonymized-but-runnable tools
- Re-host the tool and dataset behind an anonymizing service; strip repository owner, commit
author metadata, and any path revealing your identity (
/home/<you>/, institutional URLs). - Include a minimal run path: exact commands, expected inputs, and a small sample so a reviewer can execute the automation without your machine.
- Pin the environment: dependencies with versions, a container or lockfile, and the exact tool commit — automated-SE tools rot fast against moving toolchains.
Provenance pinning (do this at collection time)
For the tool:
- Exact commit SHA, build instructions, dependency versions, and configuration/flags used in the experiments (including seeds for randomized components).
For subject systems and datasets:
- Names, versions, and SHAs of every subject; the corpus extraction date; query/filter criteria; and any manual labeling protocol with inter-rater agreement.
- A regeneration script and a versioned snapshot — live scraping re-samples a moving target.
For LLM-based components:
- Model identifiers and dates, prompts, decoding settings, and cached raw outputs so the artifact reproduces rather than calls a live, drifting API.
Show full SKILL.md (148 more words)Show less
Reproducibility failure modes (ASE-specific)
| Failure | Consequence | Prevention |
|---|---|---|
| Tool needs your exact machine | Reviewers cannot run it; artifact fails | Container/lockfile + minimal run path |
| Subjects unpinned (branch, not SHA) | Numbers cannot be reproduced | Record SHAs + extraction date at collection |
| LLM outputs uncached | Re-runs drift; comparison invalid | Cache outputs; record model IDs/dates |
| Data Availability outside the 10 pages | Policy violation | Place it after Conclusions, inside the budget |
| Identity leak in artifact | Anonymity violation | Scrub owner/metadata; re-host anonymized |
From submission to the ACM badges
The submission-time artifact and the post-acceptance badge artifact are the same package matured.
ASE offers Artifacts Available and Artifacts Reusable badges (ACM scheme); staging for them
now avoids a scramble later (see ase-artifact-evaluation):
- Available — deposit in a DOI-issuing archive (Zenodo / figshare / Software Heritage) with an open license.
- Reusable — documentation, a clear run path, and structure that lets a stranger reuse the tool beyond reproducing your tables.
Output format
[Data Availability] present, after Conclusions, inside 10pp? matches the archive?
[Tool] commit pinned, deps versioned, container/lockfile, minimal run path?
[Subjects/data] SHAs + extraction date + selection/labeling protocol recorded?
[LLM] model IDs/dates, prompts, cached outputs?
[Anonymity] owner/metadata scrubbed; anonymized re-host?
[Badge readiness] Available (DOI+license) / Reusable (docs+run path) staged?© 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 ASE-Skills/skills/ase-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Ase 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 |
|---|---|---|---|---|---|---|
| Ase Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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xjtulyc/MedgeClaw
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 Ase Reproducibility
What does Ase Reproducibility do?
A skill your agent uses when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement…. Ase Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement, anonymized-but-runnable tools, tool and subject-system provenance pinning, cached LLM outputs, and staging for the ACM Available/Reusable artifact badges.
When should I use Ase Reproducibility?
Ase Reproducibility fits situations like: building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission; covering the mandatory Data Availability Statement; anonymized-but-runnable tools; tool and subject-system provenance pinning.
How do I install Ase Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-reproducibility -a claude-code`. Or copy the skill folder (ASE-Skills/skills/ase-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/ase-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Ase Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-reproducibility -a codex`. Or copy the skill folder (ASE-Skills/skills/ase-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/ase-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Ase 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 ase-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/ase-reproducibility, .gemini/skills/ase-reproducibility, .github/skills/ase-reproducibility and .opencode/skills/ase-reproducibility in your project.
What does Ase Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Ase Reproducibility is instructions for the agent only.
Does Ase 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 Ase 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 Ase Reproducibility use?
Ase 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 Ase Reproducibility use?
About 1.1k tokens (SKILL.md is roughly 4.3k 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 Ase Reproducibility?
Skills that share tags, products or a category with Ase 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 Ase 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.