How does this skill determine if a package needs compilation?

It inspects PyPI classifiers, metadata keywords, and source distribution contents for indicators of native extensions like C, C++, Rust, or Fortran, and checks for the presence of universal wheels.

Can I analyze specific versions of a Python package?

Yes, the skill allows you to specify a version number or defaults to the latest release to evaluate specific build requirements for that version.

How does this benefit my DevOps workflow?

By identifying complex dependencies early, you can pre-configure build environments, cache specific wheels, or allocate appropriate CPU/RAM resources for your CI/CD runners.

What does the build complexity score indicate?

The complexity score (0-10+) ranks difficulty: 0-1 represents pure Python packages, while scores of 5 or higher indicate complex native builds requiring significant system-level tools.

Python Package Build Analyzer

Name: Python Package Build Analyzer
Author: opendatahub-io

byopendatahub-io

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Despliegue y DevOps

Analyzes PyPI metadata to evaluate build complexity, compilation requirements, and wheel distribution strategies for Python packages.

Acerca de

The Python Package Build Analyzer helps developers and DevOps engineers navigate the complexities of Python dependency management. By inspecting PyPI metadata, the skill identifies whether a package requires native compilation (C, C++, Rust, or Fortran), calculates a numerical complexity score, and evaluates existing wheel coverage. It provides actionable recommendations for build strategies, resource planning, and platform-specific requirements, making it an essential tool for optimizing CI/CD pipelines and containerized build environments.

Características Principales

Numerical complexity scoring for build difficulty assessment
Detection of native extension requirements (C/C++/Rust/Fortran)
Wheel distribution analysis and platform gap identification
Automated PyPI metadata inspection and dependency analysis
12 GitHub stars
Actionable recommendations for build tools and resource allocation

Casos de Uso

Optimizing Docker build times by identifying compilation bottlenecks
Assessing cross-platform compatibility for new library integrations
Planning CI/CD infrastructure for complex data science dependencies

Acerca de

Características Principales

Numerical complexity scoring for build difficulty assessment
Detection of native extension requirements (C/C++/Rust/Fortran)
Wheel distribution analysis and platform gap identification
Automated PyPI metadata inspection and dependency analysis
12 GitHub stars
Actionable recommendations for build tools and resource allocation

Casos de Uso

Optimizing Docker build times by identifying compilation bottlenecks
Assessing cross-platform compatibility for new library integrations
Planning CI/CD infrastructure for complex data science dependencies