emnlp-reproducibility
Use when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API snapshot dates, logging decoding parameters and prompts, documenting data licensing and annotation, and reporting compute so another lab could rerun the study.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-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.
# EMNLP Reproducibility Use this before submission and again before camera-ready. At EMNLP, reproducibility is not a virtue signal appended to the paper — it is enforced at the gate: the Responsible NLP checklist is filed with every ARR submission, ARR has desk-rejected for incorrect, incomplete, or misleading answers since December 2024, and EMNLP 2025 published the completed checklist as an appendix of the paper itself. Assume your answers become part of the public record. ## Checklist as a contract Answer each section against the frozen PDF, then repair mismatches in whichever direction is honest: | Checklist area | The honest-answer test | Frequent violation | |---|---|---| | Limitations | Does §Limitations name real boundaries? | Ritual text that admits nothing | | Artifacts used | License and terms cited for every dataset/model? | "Standard benchmark" with no license line | | Artifacts created | Intended use and documentation stated? | Dataset released as a bare zip | | Computational experiments | Hyperparameters, budget, infrastructure reported? | "Details in code" with no code | | Human subjects / annotation | Instructions, recruitment, pay, consent reported? | Crowdwork tr
- Checklist as a contract
- The moving-target problem: LLM-era pinning
- Data provenance and annotation records
- Release honesty levels
- Compute and cost reporting
- Cheap habits that pay at review time
- Vignette: the number that could not be regenerated
- Output format
What does the emnlp-reproducibility skill do?
Use when hardening an EMNLP paper's reproducibility record — answering the Responsible NLP checklist truthfully under desk-reject enforcement, pinning model versions and API snapshot dates, logging decoding parameters and prompts, documenting data licensing and annotation, and reporting compute so another lab could rerun the study.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-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.