Agent-Skills.md

Agent Skills: Python Package Build Complexity Analysis

Analyze Python package build complexity by inspecting PyPI metadata. Evaluates compilation requirements, dependencies, distribution types, and provides recommendations for wheel building strategies.

UncategorizedID: opendatahub-io/ai-helpers/python-packaging-complexity

Repository

opendatahub-io/ai-helpers
opendatahub-ioLicense: Apache-2.0
1323

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pnpm dlx add-skill https://github.com/opendatahub-io/ai-helpers/tree/HEAD/helpers/skills/python-packaging-complexity

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helpers/skills/python-packaging-complexity/SKILL.md

Skill Metadata

Name
python-packaging-complexity
Description
Analyze Python package build complexity by inspecting PyPI metadata. Evaluates compilation requirements, dependencies, distribution types, and provides recommendations for wheel building strategies.

Python Package Build Complexity Analysis

This skill helps you evaluate the build complexity of Python packages by analyzing their PyPI metadata. It determines whether a package likely requires compilation, assesses build complexity, and provides recommendations for wheel building strategies.

Instructions

When a user asks about Python package build complexity, building wheels, or evaluating PyPI packages for compilation requirements:

  1. Run the PyPI inspection script using the package name and optional version:

    ./scripts/pypi_inspect.py <package_name> [version]

  2. Analyze the output and provide interpretation focusing on:

    Build Complexity Assessment

    • Compilation Requirements: Whether the package needs C/C++/Rust/Fortran compilation
    • Complexity Score: Numerical score indicating build difficulty (0-10+ scale)
    • Key Indicators: Specific classifiers, keywords, or dependencies that suggest complexity

    Distribution Analysis

    • Source Distribution Availability: Whether sdist is available for building
    • Existing Wheels: What wheel types already exist (platform-specific, universal)
    • Wheel Coverage: Gaps in wheel availability that might need custom builds

    Dependency Analysis

    • Complex Dependencies: Dependencies that themselves require compilation
    • Version Constraints: Python version requirements and compatibility
    • Transitive Complexity: How dependencies affect overall build complexity

  3. Provide actionable recommendations:

    • Whether to build from source or use existing wheels
    • Required build tools and dependencies
    • Platform-specific considerations
    • Estimated build time and resource requirements

Key Complexity Indicators

High Complexity (Score 5+)

  • Native extensions (C/C++/Rust/Fortran classifiers)
  • CUDA/GPU acceleration keywords
  • Known complex packages (torch, tensorflow, numpy, scipy)
  • Missing or limited wheel availability
  • Many compiled dependencies

Medium Complexity (Score 2-4)

  • Some compilation indicators in description/keywords
  • Platform-specific wheels only
  • Mixed dependency complexity
  • Moderate build requirements

Low Complexity (Score 0-1)

  • Pure Python packages
  • Universal wheels available
  • Simple dependencies
  • No compilation requirements

Usage Examples

Basic Package Analysis

./scripts/pypi_inspect.py torch

Interpretation: Analyze latest PyTorch version for build complexity, focusing on CUDA dependencies and compilation requirements.

Specific Version Analysis

./scripts/pypi_inspect.py numpy 1.24.3

Interpretation: Evaluate specific numpy version, explaining why numpy requires compilation and what build tools are needed.

JSON Output for Processing

./scripts/pypi_inspect.py tensorflow --json

Interpretation: Get structured data for programmatic analysis, then explain the complexity factors in plain language.

Providing Recommendations

Based on the analysis, provide specific guidance:

For High Complexity Packages

  • "This package requires significant compilation infrastructure"
  • "Building from source with customizations is recommended"
  • "Building from source will need: [specific tools]"
  • "Use pre-built wheels only when: source unavailable (proprietary) AND wheels exist on PyPI"

For Wheel Building Strategies

  • "Always build from source with customizations for maximum control"
  • "Platform-specific wheels needed for: [platforms]"
  • "Use pre-built wheels only when: source unavailable (proprietary) AND wheels exist on PyPI"
  • "Dependencies that complicate building: [list]"

For Development Planning

  • "Budget [time] for build environment setup"
  • "Consider containerized builds for reproducibility"
  • "Test on target platforms before production deployment"
  • "Monitor for new wheel releases that might eliminate build needs"

Error Handling

If the script fails or package is not found:

  • Verify package name spelling and availability on PyPI
  • Check if the package exists under a different name
  • Suggest alternative analysis approaches
  • Provide general guidance for unknown packages

Integration Notes

This skill works best when combined with:

  • python-packaging-license-finder - License information helps assess redistribution requirements
  • Package dependency analysis
  • Build environment setup
  • Continuous integration planning
  • Container build strategies