Does it support CI/CD workflows?

Yes, it provides standard GitHub Actions templates that include multi-version Python testing, Ruff linting, and Mypy type-checking.

What is the BayesFlow Packaging skill?

It is a specialized Claude Code capability designed to help developers create and manage Python packages that extend the BayesFlow framework using standardized best practices.

How does it handle public API exports?

The skill ensures every public function and class is imported in the package's __init__.py and explicitly added to the __all__ list for IDE discovery.

How are optional dependencies managed?

It uses the optional-dependencies section in pyproject.toml combined with guarded imports in the code to handle features that require additional packages.

Which directory layout does this skill use?

It strictly enforces the src-layout convention, which separates source code into a src/ directory to ensure better testing and packaging hygiene.

BayesFlow Extension Packaging

Name: BayesFlow Extension Packaging
Author: matthiaskloft

bymatthiaskloft

0•

Data Science & ML

Standardizes the creation and management of Python extension packages within the BayesFlow simulation-based inference ecosystem.

This skill provides Claude with specific domain knowledge to help developers build, maintain, and publish extension packages for the BayesFlow framework. It enforces the src-layout convention, manages public API exports through __init__.py and __all__, configures pyproject.toml for modern Python packaging, and implements guarded imports for optional dependencies. By following these standardized patterns, the skill ensures consistency across the BayesFlow ecosystem while automating the setup of GitHub Actions CI/CD workflows, linting with Ruff, and type-checking with Mypy.

Key Features

01Automated src-layout scaffolding and directory structure enforcement

02Strict __all__ export management for clean and discoverable public APIs

030 GitHub stars

04Pre-configured GitHub Actions CI matrix optimized for BayesFlow testing

05Implementation of guarded imports for optional 'extra' dependencies

06Standardized pyproject.toml configuration using modern Setuptools standards

Use Cases

01Updating existing Bayesian inference tools to comply with modern packaging standards

02Bootstrapping a new BayesFlow extension package with correct directory structure

03Managing complex dependency trees and optional extras for scientific computing

Key Features

01Automated src-layout scaffolding and directory structure enforcement

02Strict __all__ export management for clean and discoverable public APIs

030 GitHub stars

04Pre-configured GitHub Actions CI matrix optimized for BayesFlow testing

05Implementation of guarded imports for optional 'extra' dependencies

06Standardized pyproject.toml configuration using modern Setuptools standards

Use Cases

01Updating existing Bayesian inference tools to comply with modern packaging standards

02Bootstrapping a new BayesFlow extension package with correct directory structure

03Managing complex dependency trees and optional extras for scientific computing