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 reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance…
Install Ecai Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-reproducibility .claude/skills/ecai-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 "ecai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-reproducibility into .claude/skills/ecai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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/ECAI-Skills/skills/ecai-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 ecai-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-reproducibility .agents/skills/ecai-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 "ecai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-reproducibility into .agents/skills/ecai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ecai-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-reproducibility .cursor/skills/ecai-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 "ecai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-reproducibility into .cursor/skills/ecai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ECAI-Skills/skills/ecai-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 ecai-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-reproducibility .gemini/skills/ecai-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 "ecai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-reproducibility into .gemini/skills/ecai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ecai-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 ecai-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/ECAI-Skills/skills/ecai-reproducibility .github/skills/ecai-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 "ecai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-reproducibility into .github/skills/ecai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ecai-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 ecai-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/ECAI-Skills/skills/ecai-reproducibility .opencode/skills/ecai-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 "ecai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-reproducibility into .opencode/skills/ecai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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
ecai-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 434 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 reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance…
- Building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work
- SKILL.md covers Mode 1 — Theory / KR /…, Mode 2 — Empirical / ML /…, Provenance pinning (both… and Double-blind, in the…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- A seeded and cached package for empirical/ML work
What it does
Ecai Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance pinning for datasets and models, and an anonymized supplement that satisfies double-blind review inside ECAI's tight 7-page body with no separate artifact track.
Its SKILL.md is about 1.2k 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 reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work
- A seeded and cached package for empirical/ML work
- Provenance pinning for datasets and models
- An anonymized supplement that satisfies double-blind review inside ECAIs tight 7-page body with no separate artifact track
Example prompts
- “/ecai-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 (its code samples are bash).
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
Ecai Reproducibility loads about 1.2k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 434 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). 434 words, ~1,194 tokens.
Download SKILL.mdSave it as
.claude/skills/ecai-reproducibility/SKILL.md (or your agent's skills folder).- name
- ecai-reproducibility
- description
- Use when building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance pinning for datasets and models, and an anonymized supplement that satisfies double-blind review inside ECAI's tight 7-page body with no separate artifact track.
ECAI Reproducibility
ECAI reproducibility is in-band: there is no separate artifact-evaluation track, so the same reviewers who judge the paper judge whether the results and proofs are believable, from the 7-page body plus an anonymized supplement. Build the reproducibility story to survive that read — one pass, double-blind, in a short window — not a badge committee.
Because ECAI is a general-AI venue, "reproducible" means different things across its breadth. Pick the mode that matches your contribution.
Mode 1 — Theory / KR / argumentation: proofs are the artifact
- The body sketches; the supplement carries every full proof. A theorem stated without a checkable proof is a claim, not a result.
- State all assumptions explicitly (finiteness, admissibility, monotonicity, language fragment). The most common reject-driving misreading is a reviewer assuming a hidden condition.
- If the theory has a computational side (a solver, an encoding, complexity results), include a reference implementation or the exact encoding so a reviewer can re-run a small instance.
- Define objects once, precisely; ECAI's symbolic-AI reviewers check definitions against lemmas.
Mode 2 — Empirical / ML / planning: seed, cache, pin
- Fix and report seeds; report central tendency and spread across seeds, not a single lucky
run (
ecai-experiments). - Cache raw outputs (model predictions, planner traces, API responses) so results reproduce without live calls — a package that re-queries an API re-samples rather than reproduces.
- Pin provenance: dataset name and version/date, preprocessing scripts, model identifiers with dates, hardware where it affects timing.
- Provide a claim→file map: each reported table/number points to the script that regenerates it.
Provenance pinning (both modes, where applicable)
[ ] Dataset: name, version/DOI, download date, license, preprocessing script committed
[ ] Splits: exact train/val/test (or instance sets) fixed and included or scripted
[ ] Models: identifiers + dates (for hosted/LLM components); prompts/configs committed
[ ] Seeds: fixed and reported; number of runs stated
[ ] Environment: dependency versions pinned (lockfile / environment.yml / requirements)
[ ] Outputs: raw results cached so re-run does not depend on a live serviceShow full SKILL.md (180 more words)Show less
Double-blind, in the supplement too
The supplement is read under double-blind review. Anonymize it as carefully as the PDF:
# Sweep the staged supplement before zipping
grep -rniE 'university|@[a-z0-9.]+\.(edu|ac\.[a-z]+)|acknowledg|funded by|grant (no|number)' supplement/ | head
unzip -l supplement.zip | grep -Ei '\.git/|/home/|/Users/|\.DS_Store' | headStrip repository owners, institution names, funding lines, and any system named after your group. A de-anonymizing supplement can trigger a summary reject before the science is even read.
Honesty over completeness
- If data cannot be shared (privacy, licensing, industrial confidentiality — common in PAIS applications), say so and why, and share what you can (code, a synthetic sample, the protocol). A silent gap reads worse than a stated, justified limitation.
- Do not claim reproducibility you have not tested. Run the package from a clean checkout yourself before submitting.
Fit the 7-page body
Reproducibility content that a reviewer needs to judge the paper (the core proof idea, the
evaluation protocol, the key numbers) belongs in the body; full proofs, extra tables, and code
belong in the supplement. Nothing decision-critical may live only outside the 7 pages
(ecai-supplementary).
Post-acceptance
Convert the anonymized supplement into a permanent, open release — DOI-issuing archive, open
license, de-anonymized owners — and link it from the open-access camera-ready
(ecai-camera-ready).
Output format
[Mode] theory (proof appendix) / empirical (seeded+cached) / mixed
[Proof completeness] every theorem has a full checkable proof + explicit assumptions? yes/no
[Provenance] datasets/models/seeds/env pinned? gaps: <list>
[Claim map] each table/number -> regenerating file
[Anonymity] supplement clean / leaks: <where>
[Body/supplement split] nothing decision-critical outside the 7-page body
[Post-acceptance] DOI + open license + de-anonymized link planned© 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 ECAI-Skills/skills/ecai-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Ecai 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 |
|---|---|---|---|---|---|---|
| Ecai Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | 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
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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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Related
Categories
Questions about Ecai Reproducibility
What does Ecai Reproducibility do?
A skill your agent uses when building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance…. Ecai Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance pinning for datasets and models, and an anonymized supplement that satisfies double-blind review inside ECAI's tight 7-page body with no separate artifact track.
When should I use Ecai Reproducibility?
Ecai Reproducibility fits situations like: building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work; A seeded and cached package for empirical/ML work; provenance pinning for datasets and models; an anonymized supplement that satisfies double-blind review inside ECAIs tight 7-page body with no separate artifact track.
How do I install Ecai Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-reproducibility -a claude-code`. Or copy the skill folder (ECAI-Skills/skills/ecai-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/ecai-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Ecai Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-reproducibility -a codex`. Or copy the skill folder (ECAI-Skills/skills/ecai-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/ecai-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Ecai 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 ecai-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/ecai-reproducibility, .gemini/skills/ecai-reproducibility, .github/skills/ecai-reproducibility and .opencode/skills/ecai-reproducibility in your project.
What does Ecai Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Ecai Reproducibility is instructions for the agent only.
Does Ecai 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 Ecai 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 Ecai Reproducibility use?
Ecai 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 Ecai Reproducibility use?
About 1.2k tokens (SKILL.md is roughly 4.8k 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 Ecai Reproducibility?
Skills that share tags, products or a category with Ecai 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 Ecai 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.