acl-artifact-evaluation
Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-artifact-evaluation --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.
# ACL Artifact Evaluation Use this to plan the evidence package around an ACL paper. ACL has no separate artifact-badge track; instead, artifact scrutiny is folded into review through the supplement archive and Section B ("scientific artifacts") of the Responsible NLP checklist, which reviewers cross-check against the PDF. ## What counts as an artifact here - Code: training/inference scripts, evaluation harnesses, prompt templates. - Data: new corpora, annotations, filtered subsets of existing corpora, test suites, adversarial sets. - Model outputs: generations, ranked lists, logits used in analysis — often the cheapest way to make an LLM paper checkable without GPUs. - Human-subject materials: annotation guidelines, interface screenshots, consent text, compensation description. ## Submission-time packaging rules - Supplements upload as .tgz/.zip through the OpenReview form; links to tracked cloud storage are not acceptable, and any linked page must be anonymous. - Scrub identity everywhere reviewers can look: file paths, git metadata, notebook author fields, license headers, dataset hosting pages, README contact lines. - Reviewers are not required to open supplements. The paper pl
- What counts as an artifact here
- Submission-time packaging rules
- Checklist items your artifact must satisfy
- What an ACL reviewer opens first
- Vignette: packaging a multilingual benchmark submission
- Release ladder after acceptance
- Anonymization sweep, concretely
- Sizing and format sanity
- Output format
authorship trails in code and docs grep -ri "yourname\|yourlab\|university" . --include="*.py" --include="*.md" git history and remotes leak owners rm -rf .git; # or re-init a fresh repo for the archive copy notebook metadata carries usernames and kernel paths jupyter nbconvert --clear-output --inplace *.ipynb absolute paths in configs and logs grep -r "/home/\|/Users/" . | head
What does the acl-artifact-evaluation skill do?
Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-artifact-evaluation --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.