メニュー
Create and publish distributable scientific Python packages following Scientific Python community best practices. Covers pyproject.toml, src layout, Hatchling, metadata, CLI entry points, and PyPI publishing.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Encodes symusic's engineering taste across C++ core, nanobind bindings, and Python-facing APIs. Use when implementing or reviewing features to preserve performance-first architecture and ergonomic, predictable interfaces.
Decision framework for selecting and evolving third-party dependencies in symusic across C++, Python packaging, bindings, testing, and docs.
Analyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.
Writes, optimizes, and debugs C++ applications using modern C++20/23 features, template metaprogramming, and high-performance systems techniques. Use when building or refactoring C++ code requiring concepts, ranges, coroutines, SIMD optimization, or careful memory management — or when addressing performance bottlenecks, concurrency issues, and build system configuration with CMake.
Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting crashes, or performing log analysis, error investigation, or root cause analysis.
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
| name | python-packaging |
| description | Create and publish distributable scientific Python packages following Scientific Python community best practices. Covers pyproject.toml, src layout, Hatchling, metadata, CLI entry points, and PyPI publishing. |
| metadata | {"assets":["assets/pyproject-minimal.toml","assets/pyproject-full-featured.toml","assets/readme-template.md","assets/github-actions-publish.yml","assets/sphinx-conf.py","assets/.gitignore"],"references":["references/common-issues.md","references/docstrings.md","references/metadata.md","references/patterns.md"],"scripts":["scripts/cli-example.py"]} |
A comprehensive guide to creating, structuring, and distributing Python packages for scientific computing, following the Scientific Python Community guidelines. This skill focuses on modern packaging standards using pyproject.toml, PEP 621 metadata, and the Hatchling build backend.
Package Structure Selection:
START
├─ Pure Python scientific package (most common) → Pattern 1 (src/ layout)
├─ Need data files with package → Pattern 2 (data/ subdirectory)
├─ CLI tool → Pattern 5 (add [project.scripts])
└─ Complex multi-feature package → Pattern 3 (full-featured)
Build Backend Choice:
START → Use Hatchling (recommended for scientific Python)
├─ Need VCS versioning? → Add hatch-vcs plugin
├─ Simple manual versioning? → version = "X.Y.Z" in pyproject.toml
└─ Dynamic from __init__.py? → [tool.hatch.version] path
Dependency Management:
START
├─ Runtime dependencies → [project] dependencies
├─ Optional features → [project.optional-dependencies]
├─ Development tools → [dependency-groups] (PEP 735)
└─ Version constraints → Use >= for minimum, avoid upper caps
Publishing Workflow:
1. Build: python -m build
2. Check: twine check dist/*
3. Test: twine upload --repository testpypi dist/*
4. Verify: pip install --index-url https://test.pypi.org/simple/ pkg
5. Publish: twine upload dist/*
Common Task Quick Reference:
# Setup new package
mkdir -p my-pkg/src/my_pkg && cd my-pkg
# Create pyproject.toml with [build-system] and [project] sections
# Development install
pip install -e . --group dev
# Build distributions
python -m build
# Test installation
pip install dist/*.whl
# Publish
twine upload dist/*
Python packages now use standardized build systems instead of classic setup.py:
pyproject.tomlsetup.py, setup.cfg, or MANIFEST.insrc/ layoutsrc/my-sci-package/
├── pyproject.toml
├── README.md
├── LICENSE
├── src/
│ └── my_sci_package/
│ ├── __init__.py
│ ├── analysis.py
│ └── utils.py
├── tests/
│ ├── test_analysis.py
│ └── test_utils.py
└── docs/
└── index.md
See assets/pyproject-minimal.toml for a complete minimal pyproject.toml template.
See references/patterns.md for detailed package structure patterns including:
See references/metadata.md for detailed information on:
For CLI tool implementation, see scripts/cli-example.py for a complete example using Click.
Register in pyproject.toml:
[project.scripts]
sci-analyze = "my_sci_package.cli:main"
Ready-to-use templates are available in the assets/ directory:
.gitignore for scientific Python packagespyproject.toml templatepyproject.toml with all optionsSee references/docstrings.md for examples of NumPy-style docstrings and documentation best practices.
python -m build)tar -tvf dist/*.tar.gz)See references/common-issues.md for solutions to: