Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias…
Install Wsdm Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-reproducibility .claude/skills/wsdm-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 "wsdm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-reproducibility into .claude/skills/wsdm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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/WSDM-Skills/skills/wsdm-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 wsdm-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-reproducibility .agents/skills/wsdm-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 "wsdm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-reproducibility into .agents/skills/wsdm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-reproducibility .cursor/skills/wsdm-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 "wsdm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-reproducibility into .cursor/skills/wsdm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 WSDM-Skills/skills/wsdm-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 wsdm-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-reproducibility .gemini/skills/wsdm-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 "wsdm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-reproducibility into .gemini/skills/wsdm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-reproducibility .github/skills/wsdm-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 "wsdm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-reproducibility into .github/skills/wsdm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-reproducibility .opencode/skills/wsdm-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 "wsdm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-reproducibility into .opencode/skills/wsdm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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
wsdm-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 645 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias…
- Works in 3 steps: Rerunnable: public data + released code;… → Rebuildable: released code + documented… → Attested: online/production results…
- Hardening the reproducibility of a WSDM paper built on logs
- SKILL.md covers Provenance: behavioral data…, Temporal discipline, Bias assumptions are part of… and Runs, seeds, and variance, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Wsdm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias assumptions, seed and variance reporting, privacy-preserving release, and honesty tiers for results no outsider can rerun.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Hardening the reproducibility of a WSDM paper built on logs
- User-interaction data - provenance of behavioral datasets
- Temporal split discipline
- Click-bias assumptions
Example prompts
- “/wsdm-reproducibility”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Rerunnable: public data + released code; anyone can regenerate tables.
- Rebuildable: released code + documented proprietary pipeline; an insider
- Attested: online/production results reported with measurement protocol
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 yaml).
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
Wsdm Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 645 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). 645 words, ~1,676 tokens.
Download SKILL.mdSave it as
.claude/skills/wsdm-reproducibility/SKILL.md (or your agent's skills folder).- name
- wsdm-reproducibility
- description
- Use when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias assumptions, seed and variance reporting, privacy-preserving release, and honesty tiers for results no outsider can rerun.
WSDM Reproducibility
Make a web-search/data-mining paper re-derivable. WSDM has no reproducibility checklist to fill (none surfaced for current editions; 待核实 each cycle) - which raises the bar rather than lowering it, because reviewers apply the norm without giving you a form to hide behind. The venue-specific twist: WSDM evidence usually comes from behavioral data (queries, clicks, follows, purchases), and behavioral data has failure modes that generic ML reproducibility advice never mentions.
Provenance: behavioral data decays
A log dataset is a measurement of a platform at a moment - the platform's ranker, UI, and user base are all baked into it. Reproducibility therefore starts with recording what generated the data:
- Collection window, platform surface (web vs app, market/locale), and any known ranker or UI changes inside the window.
- The logging policy: what produced the exposures users could click on. A click log is a logged-policy artifact; results on it are conditional on that policy (this is the entire lesson of the position-bias and unbiased learning-to-rank literature born at this venue).
- Filtering steps with counts at every stage: bots removed, sessions segmented, minimum-activity thresholds. Two labs "using the same dataset" routinely diverge purely on preprocessing counts.
Temporal discipline
Random splits on interaction data leak the future into training. Default to time-based splits and document them to the day:
# split-manifest.yaml - ship with the artifact, cite in the paper
dataset: platform-logs-v3
train: {start: 2025-01-06, end: 2025-05-31}
valid: {start: 2025-06-01, end: 2025-06-14}
test: {start: 2025-06-15, end: 2025-06-28}
user_handling: users may span splits (temporal, not user-disjoint)
item_handling: cold items in test retained; reported separately
leakage_checks:
- no feature computed over any window overlapping valid/test
- global statistics (IDF, popularity) frozen at train end
notes: one ranker deployment change on 2025-04-12 inside train windowState whether users are shared across splits (temporal split) or disjoint (generalization-to-new-users split) - the two answer different questions and mixing them is a classic silent irreproducibility source in recommendation papers.
Bias assumptions are part of the method
If the paper estimates relevance or preference from clicks, its results depend on an exposure/position-bias model. Reproducibility means naming it:
| What you assume | What must be reported |
|---|---|
| Position bias (examination model) | The propensity model, how it was estimated, on what data |
| No exposure bias (rare, say so) | Why the setting justifies it |
| Popularity/selection bias corrected | The correction estimator and its hyperparameters |
| Offline metrics proxy online value | The known gap, plus any online evidence |
An unstated bias model makes the numbers unreproducible even with the code, because a re-implementer will pick a different default.
Show full SKILL.md (301 more words)Show less
Runs, seeds, and variance
- Report the number of runs and the seed policy for every learned component; ranking metrics on sparse test sets are noisy, and single-run nDCG deltas of under a point are routinely within seed variance.
- Give variance (std or CI) for headline comparisons; where a system-scale experiment genuinely cannot be repeated, say "single run" in the table note rather than letting the reader assume otherwise.
- Statistical tests over query/user-level paired differences beat aggregate deltas; state the unit of analysis (query, session, user) - it changes the test.
The honesty ladder for unrerunnable results
Industrial WSDM papers often include numbers nobody outside can regenerate (online A/B tests, full-traffic logs). Use graded language that matches the evidence tier, and put the tier in the paper:
- Rerunnable: public data + released code; anyone can regenerate tables.
- Rebuildable: released code + documented proprietary pipeline; an insider could regenerate, an outsider can audit the logic.
- Attested: online/production results reported with measurement protocol (traffic share, duration, metric definitions, guardrails) but not regenerable. Attested numbers support deployment claims, not method-ranking claims - do not let an A/B win stand in for a missing offline comparison.
Privacy is a reproducibility constraint, not an excuse
WSDM requires an ethical-considerations section; user-data handling belongs in
it. De-identification, aggregation thresholds, and consent/ToS basis for the
data should be stated - and any released sample must survive a re-identification
sniff test (rare queries and long-tail items are quasi-identifiers). "We cannot
release anything" is acceptable only alongside rung 2-3 evidence above and a
public-benchmark mirror where feasible (see wsdm-artifact-evaluation).
Pre-submission reproducibility sweep
Run once when experiments freeze, once on the final PDF:
[ ] Data provenance paragraph: window, surface, logging policy, filters+counts
[ ] Split manifest shipped and cited; user-sharing across splits stated
[ ] Bias/exposure model named, with estimation procedure and data
[ ] Seeds and run counts per learned component; variance on headline deltas
[ ] Unit of analysis named for every statistical test
[ ] Each result family labeled: rerunnable / rebuildable / attested
[ ] Attested results carry protocol: traffic %, duration, metric definitions
[ ] Released sample re-identification check done (rare queries, tail items)
[ ] Ethics section covers user-data basis and mitigations, specifically
[ ] Repo numbers regenerate paper tables (spot-check two tables end-to-end)Items that fail with no time to fix become limitation sentences, not silence - at a no-rebuttal venue, a disclosed gap is survivable and a discovered one usually is not.
Output format
[Provenance] window / surface / logging policy / filter counts: recorded?
[Splits] temporal manifest present; user-sharing stated: yes / no
[Bias model] named + estimation reported: yes / no / not applicable
[Variance] runs, seeds, CI/test + unit of analysis per headline table
[Tier] rerunnable / rebuildable / attested per result family
[Privacy] ethics-section coverage of user data: adequate / gaps listed© 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 WSDM-Skills/skills/wsdm-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wsdm 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 |
|---|---|---|---|---|---|---|
| Wsdm Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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 Wsdm Reproducibility
What does Wsdm Reproducibility do?
A skill your agent uses when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias…. Wsdm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias assumptions, seed and variance reporting, privacy-preserving release, and honesty tiers for results no outsider can rerun.
When should I use Wsdm Reproducibility?
Wsdm Reproducibility fits situations like: hardening the reproducibility of a WSDM paper built on logs; user-interaction data - provenance of behavioral datasets; temporal split discipline; click-bias assumptions.
How do I install Wsdm Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-reproducibility -a claude-code`. Or copy the skill folder (WSDM-Skills/skills/wsdm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wsdm-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Wsdm Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-reproducibility -a codex`. Or copy the skill folder (WSDM-Skills/skills/wsdm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wsdm-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Wsdm 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 wsdm-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/wsdm-reproducibility, .gemini/skills/wsdm-reproducibility, .github/skills/wsdm-reproducibility and .opencode/skills/wsdm-reproducibility in your project.
What does Wsdm Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Wsdm Reproducibility is instructions for the agent only.
Does Wsdm 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 Wsdm 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 Wsdm Reproducibility use?
Wsdm 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 Wsdm Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Wsdm Reproducibility?
Skills that share tags, products or a category with Wsdm 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 Wsdm 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.