bylearning-bayesian-statistics· 3 skills
Opinionated amortized Bayesian workflow with BayesFlow for simulation-based inference (SBI). Contains critical guardrails that agents will usually not apply unprompted — always consult before writing BayesFlow code. Trigger on: simulation-based inference, amortized inference, approximate Bayesian inference, BayesFlow, neural posterior estimation, posterior amortization, simulator design, prior design for SBI, offline/online simulation pipelines, uncertainty quantification from simulators, structured data encoders (sets, time series, images), or mentions of BasicWorkflow, fit_online, fit_offline, fit_disk, flow matching, diffusion model, consistency model, normalizing flow, summary networks, adapters, or simulation budgets.
$npx -y skills add learning-bayesian-statistics/baygent-skills --skill amortized-workflowInstalls into the current project.
Run `npx skills use "https://github.com/learning-bayesian-statistics/baygent-skills" --skill "learning-bayesian-statistics/baygent-skills/amortized-workflow"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/learning-bayesian-statistics/baygent-skills" that are relevant to the current task. Run `npx skills add "https://github.com/learning-bayesian-statistics/baygent-skills"` and select the relevant skills, then follow their instructions.