uai-experiments
Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-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.
# UAI Experiments Use this while the empirical design is still changeable. At UAI the object under test is usually an inference procedure — a posterior, a graph, an interval, a decision policy — so the experimental question is rarely "is accuracy higher?" and usually "is the uncertainty right, and at what cost?". Reviewers score whether claims are backed up convincingly; design the study so each c
What does the uai-experiments skill do?
Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-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.