recsys-experiments
Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments --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 Experiments Use this before submission when the empirical story is not yet locked. At RecSys the experiment section is where papers are won or lost, because the community's reproducibility culture makes reviewers read evaluation choices as a proxy for whether the gains are real. ## Experiment audit - Map each recommendation claim to a table, figure, ablation, off-policy estimate, or A/B r
What does the recsys-experiments skill do?
Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-experiments --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.