Find the perfect capability for your agent.
Generate Dockerfile and requirements.txt for containerizing web applications using PyPortfolioOpt optimization library. Use when containerizing Python apps with PyPortfolioOpt, portfolio optimization, or financial optimization that requires OSQP solver. Handles system-level build dependencies for numerical libraries.
Initialize complete Python project with comprehensive documentation, development environment, and tooling. Use when creating a new Python project from scratch.
This skill should be used when the user asks about UV (Python package manager), needs to set up Python virtual environments, install/manage Python CLI tools, run MCP servers with UVX, decide between uv tool install vs uvx, configure VS Code or IDEs for MCP server integration, migrate from pip/pipx/poetry to UV, or troubleshoot UV-related issues. Use when queries mention UV, UVX, Python package management, virtual environments, MCP servers, tool installation, or Python version management.
Check and fix lockfile drift across Poetry (Python) and pnpm (Node.js) projects.
Migrate Python modules from SDR_stochastic research code to vrp-toolkit architecture. Use when migrating files from the old codebase structure to the new three-layer architecture (Problem/Algorithm/Data layers), refactoring paper-specific code into generic implementations, or converting research notebooks into educational tutorials.
Organizes a flat backend directory structure into a clean, modular architecture with proper Python packages and updated import paths. Transforms a flat backend structure into a modular, organized architecture following Python best practices.
Python development standards including pytest, ruff, black, and mypy. Use when working with Python files, tests, or dependencies.
Use UV package manager for all Python operations instead of pip/python
Python development. Use when writing Python code, CLI tools, scripts, or services. Emphasizes stdlib, type hints, pytest toolchain, and minimal dependencies.
Python package reorganization with pyproject.toml inside package directory
Initialize Python projects with uv. Use when running uv init, checking generated files, setting uv add-bounds in pyproject.toml, verifying uv installation (including Windows), or preparing initial dependency setup.
Analyze and update Python dependencies in pyproject.toml, checking for compatibility and security vulnerabilities. Use when: updating dependencies, checking security issues, dependency analysis, version pinning, pip-audit, outdated packages.
Creates comprehensive Python library documentation including Google-style docstrings, Sphinx setup, API references, tutorials, and ReadTheDocs configuration. Use when writing docstrings, setting up Sphinx documentation, or creating user guides for Python libraries.
Work on Python experiments in packages/python_viterbo. Use for layout conventions, stage entrypoints, lint/test commands, and asset/plot handling.
Comprehensive guide for conda-forge recipe development. Handles legacy (meta.yaml) and modern (recipe.yaml) formats, linting, CI troubleshooting, and feedstock maintenance. Enhanced with patterns from real conda-forge feedstocks (2025). USE THIS SKILL WHEN: creating conda recipes, packaging Python/Rust/Go/C++ software, fixing conda-forge build failures, updating feedstocks, migrating to recipe.yaml format, setting up private channels, or troubleshooting conda-forge CI.
Python import best practices for avoiding CI failures. Use when writing imports, dealing with optional dependencies, or fixing import-time errors.
Manage Python environments using uv, venv, and pyproject.toml for dependency management
This skill provides commands for managing Python projects using `uv`, a fast Python package installer and resolver.
Python virtual environment management using uv for fast and reliable environment setup. Create, maintain, and manage Python virtual environments with dependency management. Use uv for environment creation, package installation, dependency resolution, and project configuration. Use when setting up new Python projects, managing dependencies, troubleshooting environment issues, or ensuring reproducible development environments.
