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 the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines…
Install Recsys Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-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/RecSys-Skills/skills/recsys-reproducibility .claude/skills/recsys-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 "recsys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-reproducibility into .claude/skills/recsys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-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/RecSys-Skills/skills/recsys-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 recsys-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-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/RecSys-Skills/skills/recsys-reproducibility .agents/skills/recsys-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 "recsys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-reproducibility into .agents/skills/recsys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-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 recsys-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-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/RecSys-Skills/skills/recsys-reproducibility .cursor/skills/recsys-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 "recsys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-reproducibility into .cursor/skills/recsys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-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 RecSys-Skills/skills/recsys-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 recsys-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-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/RecSys-Skills/skills/recsys-reproducibility .gemini/skills/recsys-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 "recsys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-reproducibility into .gemini/skills/recsys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-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 recsys-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 recsys-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/RecSys-Skills/skills/recsys-reproducibility .github/skills/recsys-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 "recsys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-reproducibility into .github/skills/recsys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-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 recsys-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 recsys-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/RecSys-Skills/skills/recsys-reproducibility .opencode/skills/recsys-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 "recsys-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-reproducibility into .opencode/skills/recsys-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-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
recsys-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 500 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines…
- Strengthening the reproducibility of an ACM RecSys paper
- SKILL.md covers Why recommender results drift:…, Minimum reporting block for…, The Reproducibility Track… and Vignette: a session-model…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits
What it does
Recsys Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines under an equal budget, reporting seeds and variance, avoiding sampled-metric distortion, and structuring a reproduction study with honest divergence analysis.
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
- Strengthening the reproducibility of an ACM RecSys paper
- Preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits
- Tuning baselines under an equal budget
- Reporting seeds and variance
Example prompts
- “/recsys-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
Recsys Reproducibility loads about 1.2k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 500 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). 500 words, ~1,171 tokens.
Download SKILL.mdSave it as
.claude/skills/recsys-reproducibility/SKILL.md (or your agent's skills folder).- name
- recsys-reproducibility
- description
- Use when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines under an equal budget, reporting seeds and variance, avoiding sampled-metric distortion, and structuring a reproduction study with honest divergence analysis.
RecSys Reproducibility
Reproducibility is unusually load-bearing at RecSys for two reasons. First, the venue runs a dedicated Reproducibility Track for papers that repeat, refute, or re-scope prior results and for datasets and frameworks that enable future reproduction. Second, the field's own literature documented how often reported recommender gains fail to survive a fair re-evaluation, so reviewers of regular papers read reproducibility signals as a proxy for whether the gains are real.
Why recommender results drift: the usual suspects
| Drift source | Typical symptom | Pin it by |
|---|---|---|
| Dataset version / filtering | "MovieLens" numbers differ across papers | Exact version id, k-core filter, and checksums |
| Split protocol | Session results inflated | Temporal or leave-one-last split, not random; document it |
| Baseline tuning asymmetry | Neural method "beats" everything | Equal search budget per system, grid and selected config logged |
| Sampled vs full-ranking metrics | Recall/nDCG off by a lot | Rank over the full item set, or state sampling and cutoff |
| Seeds and nondeterminism | ±0.005 nDCG run to run | Multiple seeds; report mean ± sd, not the best run |
| Metric implementation | nDCG differs at the 3rd decimal | One canonical scorer with cutoff and tie-handling recorded |
| Off-policy propensities | Counterfactual estimate irreproducible | Release logged propensities and the estimator variant (IPS/SNIPS/DR) |
A reproducibility-strong RecSys paper closes each row with an artifact, not a promise: the config file is the documentation.
Minimum reporting block for any empirical RecSys paper
Put this in the paper (it fits in ~0.3 page and pre-empts three review objections):
- Datasets with versions and filtering; train/validation/test usage; the split protocol.
- Tuning protocol: search space, budget, selection metric, validation split — symmetric across systems, baselines included.
- Metrics: which ones, the cutoff, and whether ranking is over the full item catalog or a sampled candidate set.
- Seeds: how many, and whether tables show mean, sd, and a significance test on close results.
- For any deployment or off-policy claim: the estimator, its assumption, and the logged propensities.
Show full SKILL.md (189 more words)Show less
The Reproducibility Track specifically
A reproduction paper is first-class work here, not a lesser contribution.
[reproduction scope] which prior papers, which claims, which datasets
[matched setup] identical splits, metrics, and — critically — equal tuning budget
[divergence report] where results reproduce, where they do not, and the diagnosed cause
[new context] a new domain, dataset, or baseline that tests generality
[artifact] a runnable pipeline others can extendHonest divergence is the contribution: "we could not reproduce claim X under matched tuning, and here is why" is publishable when the diagnosis is rigorous.
Vignette: a session-model reproduction
Consider reproducing three published session recommenders. Its reproducibility spine: the exact dataset versions and k-core filter, a temporal split shared across all systems, an equal tuning budget so no method is advantaged by search, full-ranking metrics at a fixed cutoff, five seeds with mean ± sd, and one honest paragraph on the one result that did not reproduce and the preprocessing difference that explains it.
Degrees of reproducibility
- Turnkey: one command regenerates each ranking table from logged seeds.
- Scripted: scripts exist but need documented manual steps or an external dataset download.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For RecSys, the offline pipeline should be turnkey because reviewers actually re-run it; a production A/B result may stay descriptive with the deviation documented. State the achieved level honestly rather than overpromising a turnkey build that fails on a clean machine.
Output format
[Claim inventory] <claim -> evidence location>
[Reproducibility status] strong / partial / weak
[Drift risks] <dataset version / split / baseline tuning / sampled metrics / seeds / propensities>
[Paper fixes] <must appear in main PDF>
[Repository fixes] <anonymous-repo additions>© 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 RecSys-Skills/skills/recsys-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Recsys 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 |
|---|---|---|---|---|---|---|
| Recsys 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 |
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.
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Related
Categories
Questions about Recsys Reproducibility
What does Recsys Reproducibility do?
A skill your agent uses when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines…. Recsys Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines under an equal budget, reporting seeds and variance, avoiding sampled-metric distortion, and structuring a reproduction study with honest divergence analysis.
When should I use Recsys Reproducibility?
Recsys Reproducibility fits situations like: strengthening the reproducibility of an ACM RecSys paper; preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits; tuning baselines under an equal budget; reporting seeds and variance.
How do I install Recsys Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-reproducibility -a claude-code`. Or copy the skill folder (RecSys-Skills/skills/recsys-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/recsys-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Recsys Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-reproducibility -a codex`. Or copy the skill folder (RecSys-Skills/skills/recsys-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/recsys-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Recsys 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 recsys-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/recsys-reproducibility, .gemini/skills/recsys-reproducibility, .github/skills/recsys-reproducibility and .opencode/skills/recsys-reproducibility in your project.
What does Recsys Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Recsys Reproducibility is instructions for the agent only.
Does Recsys 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 Recsys 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 Recsys Reproducibility use?
Recsys 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 Recsys Reproducibility use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 Recsys Reproducibility?
Skills that share tags, products or a category with Recsys 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 Recsys 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.