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.
Agent skill
Neurips Reproducibility
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute…
Install Neurips Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills neurips-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/NeurIPS-Skills/skills/neurips-reproducibility .claude/skills/neurips-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 "neurips-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NeurIPS-Skills/skills/neurips-reproducibility into .claude/skills/neurips-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neurips-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/NeurIPS-Skills/skills/neurips-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 neurips-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills neurips-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/NeurIPS-Skills/skills/neurips-reproducibility .agents/skills/neurips-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 "neurips-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NeurIPS-Skills/skills/neurips-reproducibility into .agents/skills/neurips-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neurips-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 neurips-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills neurips-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/NeurIPS-Skills/skills/neurips-reproducibility .cursor/skills/neurips-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 "neurips-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NeurIPS-Skills/skills/neurips-reproducibility into .cursor/skills/neurips-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neurips-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 NeurIPS-Skills/skills/neurips-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 neurips-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills neurips-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/NeurIPS-Skills/skills/neurips-reproducibility .gemini/skills/neurips-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 "neurips-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NeurIPS-Skills/skills/neurips-reproducibility into .gemini/skills/neurips-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neurips-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 neurips-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 neurips-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/NeurIPS-Skills/skills/neurips-reproducibility .github/skills/neurips-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 "neurips-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NeurIPS-Skills/skills/neurips-reproducibility into .github/skills/neurips-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neurips-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 neurips-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 neurips-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/NeurIPS-Skills/skills/neurips-reproducibility .opencode/skills/neurips-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 "neurips-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NeurIPS-Skills/skills/neurips-reproducibility into .opencode/skills/neurips-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neurips-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
neurips-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 495 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute…
- Strengthening NeurIPS reproducibility evidence
- SKILL.md covers Main-track reproducibility bar, MLRC route check, Checklist-to-evidence… and Reviewer-pushback patterns, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Aligning Paper Checklist answers with the paper
What it does
Neurips Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute disclosure, or deciding whether the MLRC/TMLR reproducibility route fits better than the main track or Datasets & Benchmarks track.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research and Scientific writing. 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 NeurIPS reproducibility evidence
- Aligning Paper Checklist answers with the paper
- Writing code/data instructions
- Setting random-seed and compute disclosure
Example prompts
- “/neurips-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
Neurips Reproducibility loads about 1.1k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 495 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). 495 words, ~1,060 tokens.
Download SKILL.mdSave it as
.claude/skills/neurips-reproducibility/SKILL.md (or your agent's skills folder).- name
- neurips-reproducibility
- description
- Use when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute disclosure, or deciding whether the MLRC/TMLR reproducibility route fits better than the main track or Datasets & Benchmarks track.
NeurIPS Reproducibility
Use this skill when a NeurIPS paper's claim depends on experiments, data, code, or a reproducibility argument. The immediate target is a trustworthy main-track paper; the alternative route is MLRC/TMLR when the central contribution is reproduction, replication, or generalizability of prior claims.
Main-track reproducibility bar
- State exact data splits, preprocessing, hyperparameters, selection criteria, compute resources, software versions, and random-seed protocol.
- Report uncertainty where it matters: confidence intervals, standard errors, multiple seeds, sensitivity checks, or negative findings.
- Distinguish exploratory experiments from evidence that supports the main claim.
- Make code/data availability match the checklist answer; "no" is allowed with justification, but a central open-source benchmark or dataset usually needs accessible artifacts.
- For human, private, medical, proprietary, or safety-sensitive data, document access constraints and ethical controls rather than pretending full release is possible.
MLRC route check
Consider the NeurIPS Reproducibility / MLRC track when the paper is primarily about confirming, partially reproducing, failing to reproduce, or extending a published ML result. The 2026 MLRC route requires TMLR review/acceptance before NeurIPS presentation consideration; this is not a shortcut for ordinary main-track submissions.
Checklist-to-evidence cross-check
A "yes" on the NeurIPS Paper Checklist with nothing in the paper to back it is exactly what reviewers hunt for. Run this cross-check so each reproducibility answer is honest and locatable; hedge the exact item wording to the current year's checklist.
| Checklist answer | Evidence that must exist | Failure pattern reviewers flag |
|---|---|---|
| Code released: yes | anonymous link plus run commands during review | "yes" with no commands or a dead link |
| Data released: yes | accessible split, license, and loading code | central benchmark claimed open but not provided |
| Seeds/protocol reported | seed count and aggregation rule in the text | a single run reported as if deterministic |
| Compute reported | hardware, wall-clock, and total resource budget | omitted cost behind a "trained until converged" |
| Error bars reported | intervals or std over runs on headline metrics | bold-best numbers with no variance |
A justified "no" beats an unsupported "yes". If full release is blocked by privacy, licensing, or safety, say so and document what reviewers can still verify.
Show full SKILL.md (154 more words)Show less
Reviewer-pushback patterns
| Reviewer concern | NeurIPS-specific fix |
|---|---|
| "Results may be a lucky seed" | report multiple seeds with variance, not a single point |
| "Cannot rerun your pipeline" | ship exact env, configs, and a one-command entry point in the ZIP |
| "Compute claims are unfair" | disclose budget and tune baselines under the same budget |
| "Dataset access unclear" | give license, hosting, and access steps, anonymized for review |
Worked vignette: a scaling-law claim
A paper claims a clean scaling law but reports one training run per model size with no intervals. Reviewers cannot tell signal from seed noise. The fix before submission: add at least a few seeds at the smaller sizes, plot variance bands, disclose the GPU-hours budget, and set the code-released and error-bars checklist answers to a "yes" that the appendix actually supports. If the contribution were instead reproducing someone else's published scaling law, the MLRC/TMLR route, not the main track, would be the correct home.
Output format
[Reproducibility status] Strong / adequate / weak
[Claim at risk] <result that cannot yet be reproduced>
[Needed evidence] <code/data/seed/compute/ablation/error bars/license>
[Checklist changes] <items to revise>
[Route] Main track / MLRC-TMLR / other© 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 NeurIPS-Skills/skills/neurips-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Neurips 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 |
|---|---|---|---|---|---|---|
| Neurips Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Modeling Code and Result Contractsyushui2022/MathModel-Skill | 454 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Backward Traceabilitylingzhi227/agent-research-skills | 390 | — | ~802 | Automated safety check: Pass | None | |
| Meta-model-agent Math Modeling PipelineWuXinbo-bo/Math-model-skills | 111 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Nature Data AvailabilityYuan1z0825/nature-skills | 47k | — | ~957 | Automated safety check: Pass | Apache-2.0 | |
| Paper Pipeline Assemblylingzhi227/agent-research-skills | 390 | — | ~971 | Automated safety check: Pass | None |
Similar skills
Backward Traceability
lingzhi227/agent-research-skills
Makes each number in a LaTeX paper link back to the code line that produced it, using hypertarget and hyperlink tags and compile-time `\num` formulas.
Meta-model-agent Math Modeling Pipeline
WuXinbo-bo/Math-model-skills
Runs a staged pipeline for mathematical modeling research and contest papers, from problem analysis and computation to paper writing, review rounds and submission checks.
Nature Data Availability
Yuan1z0825/nature-skills
Drafts or audits data and code availability statements, dataset access routes, repository plans and FAIR metadata for Nature-style manuscripts.
Paper Pipeline Assembly
lingzhi227/agent-research-skills
Orchestrates a research paper from literature review through code, experiments, figures, tables, writing and review, with state passed between phases and checkpoints to resume.
Icml Reviewer
sundial-org/skills
Paper reviewer that evaluates machine learning research projects following official ICML reviewer guidelines.
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Categories
Questions about Neurips Reproducibility
What does Neurips Reproducibility do?
A skill your agent uses when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute…. Neurips Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute disclosure, or deciding whether the MLRC/TMLR reproducibility route fits better than the main track or Datasets & Benchmarks track.
When should I use Neurips Reproducibility?
Neurips Reproducibility fits situations like: strengthening NeurIPS reproducibility evidence; aligning Paper Checklist answers with the paper; writing code/data instructions; setting random-seed and compute disclosure.
How do I install Neurips Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-reproducibility -a claude-code`. Or copy the skill folder (NeurIPS-Skills/skills/neurips-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/neurips-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Neurips Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-reproducibility -a codex`. Or copy the skill folder (NeurIPS-Skills/skills/neurips-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/neurips-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Neurips 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 neurips-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/neurips-reproducibility, .gemini/skills/neurips-reproducibility, .github/skills/neurips-reproducibility and .opencode/skills/neurips-reproducibility in your project.
What does Neurips Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Neurips Reproducibility is instructions for the agent only.
Does Neurips 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 Neurips 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 Neurips Reproducibility use?
Neurips 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 Neurips Reproducibility use?
About 1.1k tokens (SKILL.md is roughly 4.2k 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 Neurips Reproducibility?
Skills that share tags, products or a category with Neurips Reproducibility: Modeling Code and Result Contracts (yushui2022/MathModel-Skill, 454 stars), Backward Traceability (lingzhi227/agent-research-skills, 390 stars), Meta-model-agent Math Modeling Pipeline (WuXinbo-bo/Math-model-skills, 111 stars) and Nature Data Availability (Yuan1z0825/nature-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Neurips 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.