{"slug":"brycewang-stanford-bayesian-workflow","source_name":"brycewang-stanford/bayesian-workflow","name":"Brycewang Stanford/Bayesian Workflow","description":"Opinionated Bayesian modeling workflow with PyMC and ArviZ. Contains critical guardrails (nutpie sampler, prior/posterior predictive checks, LOO-PIT calibration, prior sensitivity checks, 94% HDI, non-centered parameterizations, reproducible seeds) that agents won't apply unprompted — always consult before writing Bayesian model code. Trigger on: building probabilistic/Bayesian models, prior elicitation, MCMC inference, convergence diagnostics (divergences, R-hat, ESS), model comparison (LOO-CV,","version":1,"lift":{"pass_rate_delta_pts":27.27,"pass_rate_pct":95.5,"total_cases":22,"passed_cases":21,"tokens_delta_pct":111.2,"turns_delta_pct":0,"verdict":"mixed","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-07T02:12:13.155975+00:00"},"skill_score":0.9545,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":27.27,"with_pass_pct":95.5,"without_pass_pct":68.2,"tokens_delta_pct":111.2,"turns_delta_pct":0,"total_cases":22,"cases_aggregated":22,"verdict":"mixed","never_hurt":false,"completed_at":"2026-08-07T02:12:13.155975+00:00","run_id":"9422439f-3b41-446a-b77a-1f727a46d459","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"caution","safety_status":"flagged","intent_verdict":"safe","content_status":"clean","indexable":false},"license":"MIT","install_count":0,"manifest_hash":"6ab87400bd35d5576b64498eb3dafa7e740dd47181eef80bd854cf63c4f267a2","raw_url":"https://app.decimal.ai/s/brycewang-stanford-bayesian-workflow/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/brycewang-stanford-bayesian-workflow"}