naacl-artifact-evaluation
Use when packaging datasets, models, prompts, or annotation materials for a NAACL-bound submission — building the artifact around the Responsible NLP checklist's artifact questions, documenting provenance and licensing for language data, and handling community-owned or Indigenous-language resources correctly.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-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.
# NAACL Artifact Evaluation NAACL has no separate artifact-badging track; artifacts are judged inside the ARR review itself, through the supplement upload and section B of the Responsible NLP checklist ("scientific artifacts"). That placement matters: your artifact documentation is not an optional extra but a set of sworn answers reviewers cross-examine against the PDF. ## What the artifact must let a reviewer do - **Trace provenance.** Where did every corpus come from, under what license, and does your use match the terms and the creators' intent? - **Inspect the instrument.** Prompts, annotation guidelines, interface screenshots, and pay rates are artifacts too — a human-evaluation claim without its instrument is unverifiable. - **Rerun the cheap parts.** Scoring scripts and metric code should execute from the archive alone; nobody will retrain your model, but everybody can re-score your outputs if you include them. - **Audit the data card.** Language varieties, dialect coverage, speaker demographics where relevant, known gaps, and intended use. ## Language-data documentation ladder | Data situation | Minimum documentation for a NAACL reviewer | Extra step | |---|---|---| | Stand
- What the artifact must let a reviewer do
- Language-data documentation ladder
- Anonymous packaging that survives inspection
- Vignette: a Quechua-Spanish parallel corpus package
- Cycle-volatile mechanics
- Post-acceptance conversion
- Output format
What does the naacl-artifact-evaluation skill do?
Use when packaging datasets, models, prompts, or annotation materials for a NAACL-bound submission — building the artifact around the Responsible NLP checklist's artifact questions, documenting provenance and licensing for language data, and handling community-owned or Indigenous-language resources correctly.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-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.