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 hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API…
Install Emnlp Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-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/EMNLP-Skills/skills/emnlp-reproducibility .claude/skills/emnlp-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 "emnlp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-reproducibility into .claude/skills/emnlp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-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/EMNLP-Skills/skills/emnlp-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 emnlp-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-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/EMNLP-Skills/skills/emnlp-reproducibility .agents/skills/emnlp-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 "emnlp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-reproducibility into .agents/skills/emnlp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-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 emnlp-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-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/EMNLP-Skills/skills/emnlp-reproducibility .cursor/skills/emnlp-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 "emnlp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-reproducibility into .cursor/skills/emnlp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-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 EMNLP-Skills/skills/emnlp-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 emnlp-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-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/EMNLP-Skills/skills/emnlp-reproducibility .gemini/skills/emnlp-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 "emnlp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-reproducibility into .gemini/skills/emnlp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-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 emnlp-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 emnlp-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/EMNLP-Skills/skills/emnlp-reproducibility .github/skills/emnlp-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 "emnlp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-reproducibility into .github/skills/emnlp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-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 emnlp-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 emnlp-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/EMNLP-Skills/skills/emnlp-reproducibility .opencode/skills/emnlp-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 "emnlp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-reproducibility into .opencode/skills/emnlp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-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
emnlp-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 773 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API…
- Works in 3 steps: Rerunnable — one script per table,… → Rebuildable — code and data released… → Documented — closed data or models…
- Hardening an EMNLP papers reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement
- SKILL.md covers Checklist as a contract, The moving-target problem:…, Data provenance and annotation… and Release honesty levels, plus 4 more sections
- Calls pip
What it does
Emnlp Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API snapshot dates, logging decoding parameters and prompts, documenting data licensing and annotation, and reporting compute so another lab could rerun the study.
Its SKILL.md is about 1.7k 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
- Hardening an EMNLP papers reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement
- Pinning model versions and API snapshot dates
- Logging decoding parameters and prompts
- Documenting data licensing and annotation
Example prompts
- “/emnlp-reproducibility”
Requirements
- Python 3
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Rerunnable — one script per table, pinned environment, seeds fixed; a stranger
- Rebuildable — code and data released with documented manual steps.
- Documented — closed data or models prevent release, but the paper specifies
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
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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
Emnlp Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 773 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). 773 words, ~1,665 tokens.
Download SKILL.mdSave it as
.claude/skills/emnlp-reproducibility/SKILL.md (or your agent's skills folder).- name
- emnlp-reproducibility
- description
- Use when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API snapshot dates, logging decoding parameters and prompts, documenting data licensing and annotation, and reporting compute so another lab could rerun the study.
EMNLP Reproducibility
Use this before submission and again before camera-ready. At EMNLP, reproducibility is not a virtue signal appended to the paper — it is enforced at the gate: the Responsible NLP checklist is filed with every ARR submission, ARR has desk-rejected for incorrect, incomplete, or misleading answers since December 2024, and EMNLP 2025 published the completed checklist as an appendix of the paper itself. Assume your answers become part of the public record.
Checklist as a contract
Answer each section against the frozen PDF, then repair mismatches in whichever direction is honest:
| Checklist area | The honest-answer test | Frequent violation |
|---|---|---|
| Limitations | Does §Limitations name real boundaries? | Ritual text that admits nothing |
| Artifacts used | License and terms cited for every dataset/model? | "Standard benchmark" with no license line |
| Artifacts created | Intended use and documentation stated? | Dataset released as a bare zip |
| Computational experiments | Hyperparameters, budget, infrastructure reported? | "Details in code" with no code |
| Human subjects / annotation | Instructions, recruitment, pay, consent reported? | Crowdwork treated as free magic |
A "yes" the PDF cannot support is now a desk-reject vector — the only checklist strategy is making the paper actually deserve its answers.
The moving-target problem: LLM-era pinning
The reproducibility hazard specific to modern NLP is that the measured object changes under you. Pin everything that can drift:
# repro-pin.yaml — one block per reported experiment
model: <exact identifier, e.g. open-weights checkpoint hash or API model string>
queried: 2026-04-18 .. 2026-05-02 # API results are dated observations
decoding: {temperature: 0.0, top_p: 1.0, max_tokens: 512, stop: ["\n\n"]}
prompt_file: prompts/nli_zero_shot_v3.txt # verbatim, versioned
data: {name: ..., version: ..., split: test, license: CC-BY-4.0}
seeds: [13, 42, 271, 828, 1729]
hardware: 4x A100-80GB, ~11 GPU-hours total
metric_impl: sacrebleu 2.4.0 / evaluate 0.4.2 # scores differ across implementationsTwo entries deserve emphasis. Query dates: an API model result without dates is a claim about a system that no longer exists. Metric implementation: BLEU, ROUGE, and even F1 vary across implementations and preprocessing; name the package and version or the number is not comparable to anyone else's.
Data provenance and annotation records
- Every corpus: source, version, license, and whether its terms permit your use and redistribution — the checklist asks, and "found on the internet" fails.
- Every annotation effort: guidelines (released verbatim), annotator recruitment and compensation, agreement statistics, and adjudication procedure. These double as method details — agreement is evidence about the construct, not HR paperwork.
- Preprocessing is part of the dataset: tokenization, filtering, deduplication, and class balancing decisions all change results; script them, don't prose them.
Release honesty levels
State the level you actually achieve rather than aspirationally overpromising:
- Rerunnable — one script per table, pinned environment, seeds fixed; a stranger reproduces the numbers modulo hardware nondeterminism.
- Rebuildable — code and data released with documented manual steps.
- Documented — closed data or models prevent release, but the paper specifies enough (prompts, configs, dates, metrics) for a faithful reimplementation.
Level 3 with honest reasons outperforms a broken claim of level 1 in review: NLP
reviewers do try to run things, and a repository that fails on pip install converts a
reproducibility strength into a credibility wound.
Show full SKILL.md (337 more words)Show less
Compute and cost reporting
The checklist's computational-experiments section expects infrastructure and budget answers, and the field increasingly reads them as science rather than logistics:
- Report total compute for the reported experiments and, separately, the rough multiplier for the search that found them — a method needing 40 exploratory runs to hit its configuration is different work to reproduce than one needing 4.
- For API experiments, report call counts and token volumes; "we evaluated GPT-x on the benchmark" hides a cost that determines who can replicate you.
- Name the hardware class and wall-clock, not just GPU-hours; memory limits gate reproduction more often than FLOPs.
- If a result exists because a large training run cannot be repeated (one seed on the big model, five on the small), say so where the result is reported, not only in the checklist.
Cheap habits that pay at review time
- Emit result tables from logged runs programmatically; hand-transcribed numbers drift from their logs and reviewers who ask for logs notice.
- Keep a
RESULTS.mdmapping every number in the paper to a run ID. - Store the exact prompt for every reported LLM number at generation time — prompts reconstructed later are fiction with good intentions.
- When randomness is genuinely irreducible (API nondeterminism), quantify it: repeat a subset of calls and report observed dispersion.
Vignette: the number that could not be regenerated
A submission reports 71.4 F1 for its main configuration. During the response window a reviewer asks for the per-language breakdown; the rerun produces 70.6. The cause is archaeological: the 71.4 came from a notebook using a since-edited prompt file, on a dataset version replaced in April. Nothing was dishonest — and nothing was pinned. The paper now faces a window where every number is suspect. The prevention costs one habit: no result enters the draft except from a logged run with a pinned config, and the log ID rides along in a comment next to the table. Papers built this way answer breakdown requests in an hour and gain credibility from the speed itself.
Output format
[Checklist audit] <section -> supported / mismatch -> fix>
[Pinning status] <models, dates, decoding, prompts, metrics: pinned or drifting>
[Provenance record] <corpus/annotation items missing license or agreement data>
[Release level] rerunnable / rebuildable / documented — as stated vs actual
[Repair queue] <ordered, cheapest-first>© 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 EMNLP-Skills/skills/emnlp-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Emnlp 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 |
|---|---|---|---|---|---|---|
| Emnlp Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | 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 |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Emnlp Reproducibility
What does Emnlp Reproducibility do?
A skill your agent uses when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API…. Emnlp Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API snapshot dates, logging decoding parameters and prompts, documenting data licensing and annotation, and reporting compute so another lab could rerun the study.
When should I use Emnlp Reproducibility?
Emnlp Reproducibility fits situations like: hardening an EMNLP papers reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement; pinning model versions and API snapshot dates; logging decoding parameters and prompts; documenting data licensing and annotation.
How do I install Emnlp Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-reproducibility -a claude-code`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/emnlp-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Emnlp Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-reproducibility -a codex`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/emnlp-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Emnlp 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 emnlp-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/emnlp-reproducibility, .gemini/skills/emnlp-reproducibility, .github/skills/emnlp-reproducibility and .opencode/skills/emnlp-reproducibility in your project.
What does Emnlp Reproducibility need to run?
Going by SKILL.md and its folder, Emnlp Reproducibility needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Emnlp Reproducibility access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Emnlp 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 Emnlp Reproducibility use?
Emnlp 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 Emnlp Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 Emnlp Reproducibility?
Skills that share tags, products or a category with Emnlp 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 Emnlp 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.