naacl-reproducibility
Use when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates, making multilingual evaluation re-runnable, and stating an honest release level instead of an aspirational one.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# NAACL Reproducibility Reproducibility at NAACL is enforced through a document, not a badge: the Responsible NLP checklist travels with the submission, reviewers read it against the paper, and answers contradicted by the PDF are grounds for rejection without review under current ARR policy. The working stance: every checklist answer is a claim you are prepared to defend in the author response. ##
What does the naacl-reproducibility skill do?
Use when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates, making multilingual evaluation re-runnable, and stating an honest release level instead of an aspirational one.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.