What is the benefit of the src-layout convention in this skill?

The src-layout ensures that the package can only be imported after a proper installation, which prevents common development errors and ensures that tests run against the installed version of the package.

How does the skill handle public API exports?

It enforces the use of __all__ in __init__.py files, ensuring that all public functions and classes are visible to IDE autocompletion and accessible via star imports.

Does it support optional dependencies?

Yes, it provides standardized patterns for defining optional extras in pyproject.toml and implementing guarded imports in code to prevent crashes when optional features are missing.

What CI/CD tools does this skill configure?

It generates standard GitHub Actions workflows specifically tailored for BayesFlow, including multi-version Python matrices and Keras backend configurations.

BayesFlow Packaging Assistant

Name: BayesFlow Packaging Assistant
Author: matthiaskloft

bymatthiaskloft

0•

Data Science & ML

Automates the management of BayesFlow extension packages including directory structures, API exports, and CI/CD configuration.

The BayesFlow Packaging skill provides Claude with domain-specific expertise for building and maintaining BayesFlow extension modules. It enforces the src-layout convention, ensures robust public API management via strict __all__ exports, and automates the creation of pyproject.toml files with standardized dependencies and optional extras. This skill is particularly useful for developers within the BayesFlow ecosystem who need to maintain consistency across multiple packages, manage complex versioning patterns, and set up standardized GitHub Actions CI pipelines for scientific computing environments.

Key Features

01Standardized src-layout project structure generation

02CI/CD matrix configuration for GitHub Actions

030 GitHub stars

04Safe implementation of optional dependency extras and guarded imports

05Automated __all__ management for consistent API visibility

06Template-driven pyproject.toml configuration for BayesFlow extensions

Use Cases

01Initializing a new BayesFlow extension with compliant package architecture

02Configuring multi-version Python testing environments for scientific packages

03Adding new public modules while ensuring correct exports and IDE compatibility

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add matthiaskloft/claude-skills bayesflow-packaging

For use in Claude.ai and ChatGPT

Key Features

01Standardized src-layout project structure generation

02CI/CD matrix configuration for GitHub Actions

030 GitHub stars

04Safe implementation of optional dependency extras and guarded imports

05Automated __all__ management for consistent API visibility

06Template-driven pyproject.toml configuration for BayesFlow extensions

Use Cases

01Initializing a new BayesFlow extension with compliant package architecture

02Configuring multi-version Python testing environments for scientific packages

03Adding new public modules while ensuring correct exports and IDE compatibility

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add matthiaskloft/claude-skills bayesflow-packaging

For use in Claude.ai and ChatGPT