uai-related-work
Use when positioning a UAI submission within the probabilistic reasoning, graphical-model, causality, and Bayesian ML literature, covering PMLR archival status of recent UAI volumes, double-blind self-citation discipline, concurrent arXiv and workshop versions, and the cross-community citation coverage UAI reviewers check first.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-related-work --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.
# UAI Related Work Use this to audit positioning before submission. UAI sits at a junction of communities — ML conferences, statistics, causal inference, and the older probabilistic-AI tradition — and its reviewers typically belong to at least two of them. Related-work failures here are usually coverage failures: the paper positions against one community and gets reviewed by another. ## The lanes
What does the uai-related-work skill do?
Use when positioning a UAI submission within the probabilistic reasoning, graphical-model, causality, and Bayesian ML literature, covering PMLR archival status of recent UAI volumes, double-blind self-citation discipline, concurrent arXiv and workshop versions, and the cross-community citation coverage UAI reviewers check first.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-related-work --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.