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 strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and…
Install Acl Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-reproducibility .claude/skills/acl-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 "acl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-reproducibility into .claude/skills/acl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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/ACL-Skills/skills/acl-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 acl-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-reproducibility .agents/skills/acl-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 "acl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-reproducibility into .agents/skills/acl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-reproducibility .cursor/skills/acl-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 "acl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-reproducibility into .cursor/skills/acl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 ACL-Skills/skills/acl-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 acl-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-reproducibility .gemini/skills/acl-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 "acl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-reproducibility into .gemini/skills/acl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-reproducibility .github/skills/acl-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 "acl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-reproducibility into .github/skills/acl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-reproducibility .opencode/skills/acl-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 "acl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-reproducibility into .opencode/skills/acl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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
acl-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 574 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and…
- Works in 4 steps: Grep the paper for every number that a… → Check the supplement actually contains… → Confirm Limitations mentions the… → …
- Strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review
- SKILL.md covers The checklist as a claims audit, Reporting floor for the modern…, Contamination and leakage… and Variance discipline, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Acl Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.
Its SKILL.md is about 1.4k 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 and Natural language processing. 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 reproducibility evidence for an ACL paper reviewed through ACL Rolling Review
- Covering the Responsible NLP checklist end to end
- Hyperparameter and compute reporting
- Prompt and decoding disclosure for LLM experiments
Example prompts
- “/acl-reproducibility”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Grep the paper for every number that a checklist item claims exists
- Check the supplement actually contains what Sections B/C reference.
- Confirm Limitations mentions the weaknesses your own experiments exposed;
- Re-answer Section E honestly after the final writing pass — late-stage
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
Acl Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 574 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). 574 words, ~1,450 tokens.
Download SKILL.mdSave it as
.claude/skills/acl-reproducibility/SKILL.md (or your agent's skills folder).- name
- acl-reproducibility
- description
- Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.
ACL Reproducibility
Use this before an ARR deadline and again at camera-ready. At ACL the reproducibility instrument is the Responsible NLP checklist: it is mandatory, reviewers read it alongside the paper, and ARR policy makes incorrect or misleading checklist content a desk-rejection ground. Treat it as a claims audit, not paperwork.
The checklist as a claims audit
- Section A: a real Limitations discussion and a risks discussion — reviewers are told honest limitations must not be penalized, so under-disclosing is strictly worse than disclosing.
- Section B: every dataset and model you used needs citation, version,
license, and intended-use consistency (see
acl-artifact-evaluation). - Section C: computational experiments — parameters, budget, infrastructure, hyperparameter search, and descriptive statistics with error bars.
- Section D: human annotators/participants — instructions, pay, consent, ethics-board status, demographics where relevant.
- Section E: AI assistants used in research, coding, or writing.
Every "yes" answer should carry a section/appendix pointer; every "N/A" should survive a hostile reading of the paper.
Reporting floor for the modern NLP paper
| Experiment type | Minimum disclosure that survives ACL review |
|---|---|
| Fine-tuned models | Model + version, seeds, LR/schedule, epochs, selection criterion, dev-set use, runs count |
| Prompted LLMs | Exact prompts, decoding params (temperature, top-p, max tokens), model snapshot date/version, n samples |
| API-based closed models | Access dates, version string, cost/queries, caching strategy, note on irreproducibility risk |
| Human evaluation | Instructions, item counts, raters per item, agreement statistic, pay |
| New metrics | Implementation source, correlation evidence, code in supplement |
Contamination and leakage auditing
- State which evaluation sets could plausibly appear in pretraining corpora and what you did about it: n-gram overlap scans, canary checks, dataset release date vs model cutoff reasoning.
- For benchmarks you release, record a creation date and content hash so future contamination is auditable.
- For claimed generalization, verify the test languages/domains genuinely weren't leaked through translation or paraphrase of training data.
Variance discipline
- Single-run leaderboard deltas are the classic ACL review complaint. Report mean and deviation over multiple seeds or prompt paraphrases, and say in the caption what the interval is.
- When compute makes many runs impossible, say so, quantify what you could (e.g., variance on the smallest model), and scope claims accordingly — checklist Section C expects the compute budget stated either way.
Show full SKILL.md (215 more words)Show less
Consistency sweep before submission
- Grep the paper for every number that a checklist item claims exists (error bars, splits, licenses, pay). Missing → fix paper or answer.
- Check the supplement actually contains what Sections B/C reference.
- Confirm Limitations mentions the weaknesses your own experiments exposed; reviewers notice when the Limitations section dodges the obvious one.
- Re-answer Section E honestly after the final writing pass — late-stage AI-assisted rewriting counts.
Degrees of reproducibility to declare
turnkey : one script re-scores released outputs / reruns the pipeline
scripted : code + configs released; needs GPUs, keys, or gated data
descriptive: enough prose + prompts that a motivated lab could rebuild it
closed : hinges on private data or deprecated APIs — say so in LimitationsDeclare the level you actually achieve. At ACL, releasing model outputs is the cheap trick that upgrades many LLM papers from descriptive to turnkey, because re-scoring needs no compute.
Prompt-disclosure block that satisfies reviewers
A reusable appendix pattern for each prompted experiment:
Experiment: Table 3, zero-shot NLI
Model: <name + exact version/snapshot + access date>
Decoding: temperature=0.0, top_p=1.0, max_tokens=16
Prompt (verbatim, incl. whitespace):
"Premise: {premise}\nHypothesis: {hypothesis}\n
Answer entailment, neutral, or contradiction:"
Paraphrases: 5 variants (App. D.2); reported number = mean over variants
Post-processing: first-token match, case-insensitive; ties -> neutral
Failures: non-parseable outputs counted as errors (2.3% of calls)The last two lines — parsing rules and non-parseable handling — are where most "we could not reproduce the number" disputes actually originate.
Cheap wins ranked by effort
- Release model outputs alongside code (near-zero cost, enables re-scoring).
- Log and report seeds + run counts in every caption while runs are fresh.
- Pin dataset versions/commits in the bibliography and README now, not at camera-ready when the version has silently moved.
- Write the compute paragraph (GPUs, hours, total runs incl. failed) the week the experiments finish.
- Save the exact evaluation-script commit used for headline numbers.
Output format
[Checklist status] consistent / gaps found / contradicts paper
[Section-by-section] <A/B/C/D/E: pass or missing items>
[LLM disclosure] <prompts/decoding/version/date status>
[Contamination stance] <audit done / reasoned / unaddressed>
[Variance reporting] <runs, intervals, caption clarity>
[Fixes] <paper edits vs supplement additions, ordered>© 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 ACL-Skills/skills/acl-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Acl 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 |
|---|---|---|---|---|---|---|
| Acl Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | 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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Figure Style
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Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
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Categories
Questions about Acl Reproducibility
What does Acl Reproducibility do?
A skill your agent uses when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and…. Acl Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.
When should I use Acl Reproducibility?
Acl Reproducibility fits situations like: strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review; covering the Responsible NLP checklist end to end; hyperparameter and compute reporting; prompt and decoding disclosure for LLM experiments.
How do I install Acl Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-reproducibility -a claude-code`. Or copy the skill folder (ACL-Skills/skills/acl-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/acl-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Acl Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-reproducibility -a codex`. Or copy the skill folder (ACL-Skills/skills/acl-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/acl-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Acl 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 acl-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/acl-reproducibility, .gemini/skills/acl-reproducibility, .github/skills/acl-reproducibility and .opencode/skills/acl-reproducibility in your project.
What does Acl Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Acl Reproducibility is instructions for the agent only.
Does Acl 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 Acl 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 Acl Reproducibility use?
Acl 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 Acl Reproducibility use?
About 1.4k tokens (SKILL.md is roughly 5.8k 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 Acl Reproducibility?
Skills that share tags, products or a category with Acl 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 Acl 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.