uai-reproducibility
Use when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure, compute reporting, and honest code-availability statements, since UAI strongly encourages released code and data and reviews whether claims are convincingly backed.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-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.
# UAI Reproducibility UAI's 2026 CFP did not impose a formal reproducibility checklist (one may appear later — 待核实 each cycle), but it strongly encouraged code and data availability and instructed reviewers to judge whether claims are backed up convincingly. At this venue "convincing" has a technical meaning: stochastic-inference results carry diagnostics, not just point estimates. This skill turns that norm into an audit. ## The UAI-specific bar Reproducibility questions at a probabilistic-inference venue go one level deeper than "can I rerun the script": - A sampler that reproduces the same posterior mean but different tail quantiles has not reproduced the paper — report and check the diagnostics that detect this (R-hat, ESS, divergent transitions where relevant). - A variational result is reproducible when the ELBO trajectory and the selected restart are recoverable, not merely the final metric; multi-restart selection rules must be stated. - Calibration claims reproduce only if the binning scheme, the split used for recalibration, and the α grid are all pinned down; empirical coverage moves with all three. - Causal-discovery results depend on graph generation as much as on the
- The UAI-specific bar
- Determinism ledger
- Disclosure map
- Diagnostic quick reference
- Honesty over completeness
- Where reproducibility evidence lives
- Pre-submission reproducibility drill
- Output format
What does the uai-reproducibility skill do?
Use when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure, compute reporting, and honest code-availability statements, since UAI strongly encourages released code and data and reviews whether claims are convincingly backed.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-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.