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 for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence.
Install Iclr Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-reproducibility .claude/skills/iclr-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 "iclr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-reproducibility into .claude/skills/iclr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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/ICLR-Skills/skills/iclr-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 iclr-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-reproducibility .agents/skills/iclr-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 "iclr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-reproducibility into .agents/skills/iclr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 iclr-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-reproducibility .cursor/skills/iclr-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 "iclr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-reproducibility into .cursor/skills/iclr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 ICLR-Skills/skills/iclr-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 iclr-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-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/ICLR-Skills/skills/iclr-reproducibility .gemini/skills/iclr-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 "iclr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-reproducibility into .gemini/skills/iclr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 iclr-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 iclr-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/ICLR-Skills/skills/iclr-reproducibility .github/skills/iclr-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 "iclr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-reproducibility into .github/skills/iclr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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 iclr-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 iclr-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/ICLR-Skills/skills/iclr-reproducibility .opencode/skills/iclr-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 "iclr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-reproducibility into .opencode/skills/iclr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-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
iclr-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~932 tokens
- SKILL.md length
- 393 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 for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence.
- Strengthening reproducibility for ICLR papers
- SKILL.md covers Reproducibility audit, Common ICLR weak points, The reproducibility statement… and Worked vignette, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Including seeds
What it does
Iclr Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence. Use when writing the ICLR reproducibility statement, when a reviewer says a result is not verifiable, or when mapping each representation-learning claim to a seed, split, and command so anyone reading the permanent OpenReview record can check it.
Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Strengthening reproducibility for ICLR papers
- Including seeds
- Implementation details
- Ethics statements
Example prompts
- “/iclr-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
Iclr Reproducibility loads about 932 tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 393 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). 393 words, ~932 tokens.
Download SKILL.mdSave it as
.claude/skills/iclr-reproducibility/SKILL.md (or your agent's skills folder).- name
- iclr-reproducibility
- description
- Use when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence. Use when writing the ICLR reproducibility statement, when a reviewer says a result is not verifiable, or when mapping each representation-learning claim to a seed, split, and command so anyone reading the permanent OpenReview record can check it.
ICLR Reproducibility
Use this when the paper's main claims depend on experiments, simulations, data processing, human subjects, or benchmark comparisons. ICLR reviewers are asked to evaluate rigor and reproducibility, not just headline scores.
Reproducibility audit
- Map each central claim to a table, figure, proof, appendix item, or artifact command.
- Record seeds, variance, confidence intervals, test splits, preprocessing, early stopping, hyperparameter search, model selection, and compute budget.
- Distinguish training compute from inference compute and report hardware details that affect comparability.
- Add negative results and failure cases when they explain boundary conditions.
- Check whether ethics or reproducibility statements are relevant under the current Author Guide.
- Make the appendix useful but not required for basic verification; reviewers may not inspect every appendix page.
Common ICLR weak points
- Single-seed wins on unstable benchmarks.
- Missing comparison to strong open-source baselines or recent OpenReview/arXiv work.
- Ambiguous data leakage, test-set tuning, or prompt selection.
- Scaling claims without enough model sizes, tasks, or compute reporting.
- Ablations that remove multiple mechanisms at once.
- Private data or closed APIs with no substitute verification path.
The reproducibility statement as a contract
ICLR has long pushed reproducibility statements and code release as community norms. Treat the statement as a public contract: it sits beside the paper permanently, so reviewers and later readers will hold you to it. Map every claim to something checkable.
| Claim element | What the statement should pin | Reviewer doubt it removes |
|---|---|---|
| Headline number | Seed set, split, exact command | "Did they tune on test?" |
| Architecture detail | Config file in the supplement | "Hidden trick not in the text" |
| Compute cost | Hardware and FLOPs, train vs inference | "Only works at huge scale" |
| Data pipeline | Preprocessing script and license | "Leakage between splits" |
Show full SKILL.md (114 more words)Show less
Worked vignette
A representation-learning paper reports an embedding that improves retrieval. The reproducibility
statement maps the headline metric to eval_retrieval.py --seed {0..4} --split test, names the
frozen-encoder protocol, links the anonymized config, and reports per-seed variance. When a reviewer
asks whether the gain survives a different split, the authors point to the appendix table already
covering it. The verifiable mapping turns a potential "fragile" grade into "adequate" without new runs.
Reviewer-pushback patterns
- "Cannot verify without your private data." Provide a synthetic or public-subset substitute path.
- "No variance reported." Add seed spread; ICLR reviewers distrust single-run peaks.
- "Appendix is a dump." Add an appendix map so verification does not require reading every page.
Output format
[Reproducibility grade] strong / adequate / fragile / not reviewable
[Claim-to-evidence map] <claim -> table/figure/appendix/artifact>
[Missing controls] <seeds, baselines, ablations, leakage checks>
[Compute disclosure] complete / incomplete
[Priority fixes] <smallest changes that improve review confidence>© 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 ICLR-Skills/skills/iclr-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Iclr 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 |
|---|---|---|---|---|---|---|
| Iclr Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~932 | 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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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 Iclr Reproducibility
What does Iclr Reproducibility do?
A skill your agent uses when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence. Iclr Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence.
When should I use Iclr Reproducibility?
Iclr Reproducibility fits situations like: strengthening reproducibility for ICLR papers; including seeds; implementation details; ethics statements.
How do I install Iclr Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-reproducibility -a claude-code`. Or copy the skill folder (ICLR-Skills/skills/iclr-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/iclr-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Iclr Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-reproducibility -a codex`. Or copy the skill folder (ICLR-Skills/skills/iclr-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/iclr-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Iclr 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 iclr-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/iclr-reproducibility, .gemini/skills/iclr-reproducibility, .github/skills/iclr-reproducibility and .opencode/skills/iclr-reproducibility in your project.
What does Iclr Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Iclr Reproducibility is instructions for the agent only.
Does Iclr 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 Iclr 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 Iclr Reproducibility use?
Iclr 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 Iclr Reproducibility use?
About 932 tokens (SKILL.md is roughly 3.7k 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 Iclr Reproducibility?
Skills that share tags, products or a category with Iclr 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 Iclr 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.