AgentSkillsCN

mastering-python-skill

在docs/project_notes/中建立并维护一套结构化的项目记忆系统，用于记录Bug及其解决方案、架构决策、项目关键事实，以及工作历程。当用户提出“建立项目记忆”“追踪我们的决策”“记录Bug修复”“更新项目记忆”“初始化记忆系统”等需求时，可选用此技能。同时配置CLAUDE.md与AGENTS.md，确保在不同AI编码工具之间保持记忆意识。

SKILL.md
--- frontmatter
name: mastering-python-skill
description: Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".
allowed-tools:
  - Read
  - Write
  - Bash
  - Edit
metadata:
  version: 2.1.0
  domains:
    - python
    - testing
    - packaging
    - web-development
    - async
    - security
  author: arelben
  scope: [root]
  auto_invoke: "Writing Python code"

Mastering Python Skill

Production-ready Python patterns with runnable code examples.

Contents

Workflow

Phase 1: Setup

  1. •

    Verify Python version

    bash
    python --version  # Require 3.10+, prefer 3.12+
  2. •

    Create and activate virtual environment

    bash
    python -m venv .venv && source .venv/bin/activate
  3. •

    Install dependencies

    bash
    poetry install  # or: pip install -r requirements.txt

Phase 2: Develop

  1. •

    Reference appropriate patterns:

  2. •

    Follow project structure from project-structure.md

Phase 3: Validate

  1. •

    Run quality checks

    bash
    ruff check . && ruff format --check .
mypy src/
  2. •

    Run tests with coverage

    bash
    pytest -v --cov=src --cov-report=term-missing

Phase 4: Deploy

  1. •

    Build and verify package

    bash
    python -m build && twine check dist/*
  2. •

    Deploy per docker-deployment.md or ci-cd-pipelines.md

Pre-Completion Checklist:

code
- [ ] All tests pass
- [ ] mypy reports no errors
- [ ] ruff check clean
- [ ] Coverage ≥80%
- [ ] No security warnings in dependencies

Reference Files

CategoryFilesKey Topics
Foundationssyntax-essentials, type-systems, project-structure, code-qualityVariables, type hints, generics, src layout, ruff, mypy
Patternsasync-programming, error-handling, decorators, context-managers, generatorsasync/await, exceptions, Result type, with statements, yield
Testingpytest-essentials, mocking-strategies, property-testingFixtures, parametrize, unittest.mock, Hypothesis
Web APIsfastapi-patterns, pydantic-validation, database-accessDependencies, middleware, validators, SQLAlchemy async
Packagingpoetry-workflow, pyproject-config, docker-deploymentLock files, PEP 621, multi-stage builds
Productionci-cd-pipelines, monitoring, securityGitHub Actions, OpenTelemetry, OWASP, JWT

See TOC.md for detailed topic lookup.

Sample CLI Tools

Runnable examples demonstrating production patterns:

ToolDemonstratesReference
async_fetcher.pyAsync HTTP, rate limiting, error handlingasync-programming.md
config_loader.pyPydantic settings, .env files, validationpydantic-validation.md
db_cli.pySQLAlchemy async CRUD, repository patterndatabase-access.md
code_validator.pyRun→check→fix with ruff and mypycode-quality.md
bash
# Test examples
python sample-cli/async_fetcher.py https://httpbin.org/get
python sample-cli/config_loader.py --show-env
python sample-cli/db_cli.py init --sample-data && python sample-cli/db_cli.py list
python sample-cli/code_validator.py src/

When NOT to Use

  • •Non-Python languages: Use language-specific skills
  • •ML/AI model internals: Use PyTorch/TensorFlow skills
  • •Cloud infrastructure: Use AWS/GCP skills for infra (this covers code)
  • •Legacy Python 2: Focus is Python 3.10+