recsys-artifact-evaluation
Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-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.
# RecSys Artifact Evaluation Use this for evidence packaging around RecSys. The venue does not run a separate artifact-badge process; instead the Call for Contributions expects a link to an **anonymous repository** inside the paper, and reproducibility-minded reviewers judge the paper partly by whether that repository makes the ranking claims regenerable. ## Artifact plan - Decide what a reviewer needs to trust the claim: the exact dataset version, the split script, baseline configurations, tuning grids, trained-model checkpoints, exposure/propensity logs for off-policy claims, seeds, and the evaluation code. - Keep decision-critical evidence in the paper or appendix; optional run files live in the repository, because RecSys reviewers are not obliged to open it. - Anonymize repository history, commit authors, cluster paths, license headers, and any platform or organization names. - Include a one-minute reproduction map: environment, dependencies, dataset download or identifier, commands, expected ranking numbers, runtime, seeds, and known nondeterminism. - For proprietary interaction data, give enough provenance and preprocessing detail for credible reproduction on a public dataset
- Artifact plan
- What RecSys evidence reviewers open first
- Worked vignette: packaging an off-policy study
- Calibration anchors
- Output format
What does the recsys-artifact-evaluation skill do?
Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-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.