AgentSkillsCN

python-packaging-complexity

通过检查 PyPI 元数据来分析 Python 软件包的构建复杂度。评估编译需求、依赖项、分发类型，并就轮子（wheel）构建策略提供相应建议。

SKILL.md
--- frontmatter
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.
allowed-tools: Bash Read

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:

    bash
    ./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

bash
./scripts/pypi_inspect.py torch

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

Specific Version Analysis

bash
./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

bash
./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"
  • •"Consider using pre-built wheels when available"
  • •"Building from source will need: [specific tools]"
  • •"Estimated build time: [timeframe]"

For Wheel Building Strategies

  • •"Universal wheels available - use those for maximum compatibility"
  • •"Platform-specific wheels needed for: [platforms]"
  • •"Source build recommended for: [specific scenarios]"
  • •"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