bayesflow-adapter

Create and modify BayesFlow data preprocessing adapters with schema mapping and transformations.

Updated Mar 5, 2026

Search Tags:#standardization #pipeline #adapter #data preprocessing #transforms #bayesflow

Ready to run

npx skills add https://github.com/matthiaskloft/claude-skills --skill bayesflow-adapter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bayesflow-adapter
Source: https://github.com/matthiaskloft/claude-skills/tree/main/bayesflow/skills/bayesflow-adapter
Command: npx skills add https://github.com/matthiaskloft/claude-skills --skill bayesflow-adapter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the creation and debugging of data preprocessing pipelines for BayesFlow, ensuring simulator output is correctly formatted for inference and summary networks.

Core Features & Use Cases

Schema Mapping: Organizes simulator output into inference_variables, summary_variables, and inference_conditions.
Transformations: Applies custom element-wise or cross-key transformations to data.
Standardization: Implements robust standardization using prior moments to prevent distribution shift.
Use Case: You need to prepare simulation data for a Bayesian neural network. This Skill helps you define the exact preprocessing steps, including feature engineering and standardization, to feed into your model.

Quick Start

Use the bayesflow-adapter skill to create a pipeline that standardizes the 'param' key using prior moments.

Frequently Asked Questions about bayesflow-adapter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a data preprocessing pipeline for BayesFlow simulations?▼

You build a BayesFlow data preprocessing pipeline by creating an adapter that maps simulator output into inference variables, summary variables, and inference conditions. The adapter also applies custom transformations and standardizes data using analytical prior moments.

How does standardization using prior moments prevent distribution shift in BayesFlow?▼

Standardization using prior moments prevents distribution shift by computing scaling factors from analytical prior distributions rather than the simulated data itself. This ensures the preprocessing adapter maintains consistent data distributions across training and inference.

Can I apply custom element-wise transformations to simulator output in a BayesFlow adapter?▼

Yes, you can apply custom element-wise or cross-key transformations to simulator output. The adapter supports both imperative and declarative specifications, allowing you to define feature engineering steps directly within the preprocessing pipeline.

What is the best way to map simulator output into inference variables and summary variables?▼

The best way to map simulator output is using a BayesFlow adapter to organize keys into inference variables, summary variables, and inference conditions. This schema mapping ensures the data is correctly formatted for downstream neural network consumption.

Do I need to specify my preprocessing adapter declaratively or imperatively for BayesFlow?▼

You do not need to choose exclusively; the adapter supports both imperative and declarative specifications. You can define schema mapping, transformations, and standardization steps using whichever programming style fits your workflow.

Why does my BayesFlow neural network receive incorrectly formatted simulation data?▼

Your BayesFlow network receives incorrectly formatted data when the preprocessing pipeline lacks proper schema mapping. Creating an adapter to organize simulator output into inference variables, summary variables, and conditions ensures correct formatting.