Agent skill

Python Testing

by xu-xiang in xu-xiang/everything-claude-code-zh

使用 pytest、TDD 方法论、固件(Fixtures)、模拟(Mocking)、参数化及覆盖率要求的 Python 测试策略。

MITAuto-check passedTesting & QA

Install Python Testing

skills CLI
$ npx skills add xu-xiang/everything-claude-code-zh --skill python-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xu-xiang/everything-claude-code-zh python-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/ja-JP/skills/python-testing .claude/skills/python-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
python-testing
GitHub stars
2k
Token cost
~3.9k tokens
SKILL.md length
211 words
Files
1
Skills in repo
78
Repo updated
First seen
Licence
MIT

At a glance

使用 pytest、TDD 方法论、固件(Fixtures)、模拟(Mocking)、参数化及覆盖率要求的 Python 测试策略。

  • Works in 3 steps: 红 (RED): 编写针对预期行为的失败测试 → 绿 (GREEN): 编写使测试通过的最少代码 → 重构 (REFACTOR): 在保持测试通过的同时改进代码
  • Tasks that involve Unit testing
  • SKILL.md covers 何时启用, 核心测试哲学, pytest 基础 and 固件 (Fixtures), plus 3 more sections
  • Calls pytest

What it does

Python Testing is an agent skill from xu-xiang/everything-claude-code-zh. 使用 pytest、TDD 方法论、固件(Fixtures)、模拟(Mocking)、参数化及覆盖率要求的 Python 测试策略。

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Unit testing and Test-driven development. It works with Python and pytest. The repository describes itself as: everything-claude-code 中文翻译项目:完整的 Claude Code 配置集合(agents, skills, hooks, commands, rules, MCPs)。源自 Anthropic 黑客松获胜者的实战配置,助力中文工程师高效理解与使用 Claude Code。 The licence is MIT.

When your agent uses it

  • Tasks that involve Unit testing
  • Tasks that involve Test-driven development

Example prompts

  • “/python-testing”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. 红 (RED): 编写针对预期行为的失败测试
  2. 绿 (GREEN): 编写使测试通过的最少代码
  3. 重构 (REFACTOR): 在保持测试通过的同时改进代码

What it can do on your machine

Read from SKILL.md and the folder at commit dfbf946. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pytest

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Python Testing loads about 3.9k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 211 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from xu-xiang/everything-claude-code-zh at commit dfbf946, republished under its MIT licence (© xu-xiang). 211 words, ~3,890 tokens.

Download SKILL.mdSave it as .claude/skills/python-testing/SKILL.md (or your agent's skills folder).
name
python-testing
description
使用 pytest、TDD 方法论、固件(Fixtures)、模拟(Mocking)、参数化及覆盖率要求的 Python 测试策略。

Python 测试模式

使用 pytest、TDD 方法论和最佳实践的 Python 应用程序全面测试策略。

何时启用

  • 编写新的 Python 代码时(遵循 TDD:红、绿、重构)
  • 设计 Python 项目的测试套件时
  • 审查 Python 测试覆盖率时
  • 搭建测试基础设施时

核心测试哲学

测试驱动开发 (TDD)

始终遵循 TDD 循环。

  1. 红 (RED): 编写针对预期行为的失败测试
  2. 绿 (GREEN): 编写使测试通过的最少代码
  3. 重构 (REFACTOR): 在保持测试通过的同时改进代码
python
# 步骤 1: 编写失败的测试 (RED)
def test_add_numbers():
    result = add(2, 3)
    assert result == 5

# 步骤 2: 编写最简单的实现 (GREEN)
def add(a, b):
    return a + b

# 步骤 3: 必要时进行重构 (REFACTOR)
覆盖率要求
  • 目标: 80% 以上的代码覆盖率
  • 核心路径: 必须达到 100% 覆盖
  • 使用 pytest --cov 测量覆盖率
bash
pytest --cov=mypackage --cov-report=term-missing --cov-report=html

pytest 基础

基本测试结构
python
import pytest

def test_addition():
    """测试基本加法。"""
    assert 2 + 2 == 4

def test_string_uppercase():
    """测试字符串大写转换。"""
    text = "hello"
    assert text.upper() == "HELLO"

def test_list_append():
    """测试列表追加。"""
    items = [1, 2, 3]
    items.append(4)
    assert 4 in items
    assert len(items) == 4
断言 (Assertions)
python
# 相等
assert result == expected

# 不等
assert result != unexpected

# 真值
assert result  # Truthy
assert not result  # Falsy
assert result is True  # 严格为 True
assert result is False  # 严格为 False
assert result is None  # 严格为 None

# 成员资格
assert item in collection
assert item not in collection

# 比较
assert result > 0
assert 0 <= result <= 100

# 类型检查
assert isinstance(result, str)

# 异常测试 (推荐方式)
with pytest.raises(ValueError):
    raise ValueError("error message")

# 检查异常消息
with pytest.raises(ValueError, match="invalid input"):
    raise ValueError("invalid input provided")

# 检查异常属性
with pytest.raises(ValueError) as exc_info:
    raise ValueError("error message")
assert str(exc_info.value) == "error message"

固件 (Fixtures)

基本固件使用
python
import pytest

@pytest.fixture
def sample_data():
    """提供示例数据的固件。"""
    return {"name": "Alice", "age": 30}

def test_sample_data(sample_data):
    """使用固件进行测试。"""
    assert sample_data["name"] == "Alice"
    assert sample_data["age"] == 30
带有设置/拆卸 (Setup/Teardown) 的固件
python
@pytest.fixture
def database():
    """带有设置和拆卸功能的固件。"""
    # 设置 (Setup)
    db = Database(":memory:")
    db.create_tables()
    db.insert_test_data()

    yield db  # 提供给测试使用

    # 拆卸 (Teardown)
    db.close()

def test_database_query(database):
    """测试数据库操作。"""
    result = database.query("SELECT * FROM users")
    assert len(result) > 0
固件作用域 (Fixture Scopes)
python
# 函数作用域 (默认) - 每个测试运行一次
@pytest.fixture
def temp_file():
    with open("temp.txt", "w") as f:
        yield f
    os.remove("temp.txt")

# 模块作用域 - 每个模块运行一次
@pytest.fixture(scope="module")
def module_db():
    db = Database(":memory:")
    db.create_tables()
    yield db
    db.close()

# 会话作用域 - 整个测试会话运行一次
@pytest.fixture(scope="session")
def shared_resource():
    resource = ExpensiveResource()
    yield resource
    resource.cleanup()
参数化固件
python
@pytest.fixture(params=[1, 2, 3])
def number(request):
    """参数化固件。"""
    return request.param

def test_numbers(number):
    """测试运行 3 次,每个参数一次。"""
    assert number > 0
使用多个固件
python
@pytest.fixture
def user():
    return User(id=1, name="Alice")

@pytest.fixture
def admin():
    return User(id=2, name="Admin", role="admin")

def test_user_admin_interaction(user, admin):
    """测试使用多个固件。"""
    assert admin.can_manage(user)
自动使用固件 (Autouse Fixtures)
python
@pytest.fixture(autouse=True)
def reset_config():
    """在每个测试之前自动运行。"""
    Config.reset()
    yield
    Config.cleanup()

def test_without_fixture_call():
    # reset_config 自动运行
    assert Config.get_setting("debug") is False
用于共享固件的 conftest.py
python
# tests/conftest.py
import pytest

@pytest.fixture
def client():
    """所有测试共享的固件。"""
    app = create_app(testing=True)
    with app.test_client() as client:
        yield client

@pytest.fixture
def auth_headers(client):
    """为 API 测试生成身份验证头。"""
    response = client.post("/api/login", json={
        "username": "test",
        "password": "test"
    })
    token = response.json["token"]
    return {"Authorization": f"Bearer {token}"}

参数化 (Parameterization)

基本参数化
python
@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("world", "WORLD"),
    ("PyThOn", "PYTHON"),
])
def test_uppercase(input, expected):
    """使用不同的输入运行 3 次测试。"""
    assert input.upper() == expected
多参数化
python
@pytest.mark.parametrize("a,b,expected", [
    (2, 3, 5),
    (0, 0, 0),
    (-1, 1, 0),
    (100, 200, 300),
])
def test_add(a, b, expected):
    """使用多个输入测试加法。"""
    assert add(a, b) == expected
带有 ID 的参数化
python
@pytest.mark.parametrize("input,expected", [
    ("valid@email.com", True),
    ("invalid", False),
    ("@no-domain.com", False),
], ids=["valid-email", "missing-at", "missing-domain"])
def test_email_validation(input, expected):
    """使用可读的测试 ID 进行电子邮件验证测试。"""
    assert is_valid_email(input) is expected
参数化固件 (Parameterized Fixtures)
python
@pytest.fixture(params=["sqlite", "postgresql", "mysql"])
def db(request):
    """针对多个数据库后端进行测试。"""
    if request.param == "sqlite":
        return Database(":memory:")
    elif request.param == "postgresql":
        return Database("postgresql://localhost/test")
    elif request.param == "mysql":
        return Database("mysql://localhost/test")

def test_database_operations(db):
    """测试运行 3 次,每个数据库一次。"""
    result = db.query("SELECT 1")
    assert result is not None

标记 (Markers) 与测试选择

自定义标记
python
# 标记耗时较长的测试
@pytest.mark.slow
def test_slow_operation():
    time.sleep(5)

# 标记集成测试
@pytest.mark.integration
def test_api_integration():
    response = requests.get("https://api.example.com")
    assert response.status_code == 200

# 标记单元测试
@pytest.mark.unit
def test_unit_logic():
    assert calculate(2, 3) == 5
运行特定测试
bash
# 仅运行快速测试
pytest -m "not slow"

# 仅运行集成测试
pytest -m integration

# 运行集成测试或耗时测试
pytest -m "integration or slow"

# 运行标记为单元测试但不耗时的测试
pytest -m "unit and not slow"
在 pytest.ini 中配置标记
ini
[pytest]
markers =
    slow: 标记耗时较长的测试
    integration: 标记集成测试
    unit: 标记单元测试
    django: 标记需要 Django 的测试

模拟 (Mocking) 与补丁 (Patching)

模拟函数
python
from unittest.mock import patch, Mock

@patch("mypackage.external_api_call")
def test_with_mock(api_call_mock):
    """使用模拟的外部 API 进行测试。"""
    api_call_mock.return_value = {"status": "success"}

    result = my_function()

    api_call_mock.assert_called_once()
    assert result["status"] == "success"
模拟返回值
python
@patch("mypackage.Database.connect")
def test_database_connection(connect_mock):
    """使用模拟的数据库连接进行测试。"""
    connect_mock.return_value = MockConnection()

    db = Database()
    db.connect()

    connect_mock.assert_called_once_with("localhost")
模拟异常
python
@patch("mypackage.api_call")
def test_api_error_handling(api_call_mock):
    """使用模拟的异常进行错误处理测试。"""
    api_call_mock.side_effect = ConnectionError("Network error")

    with pytest.raises(ConnectionError):
        api_call()

    api_call_mock.assert_called_once()
模拟上下文管理器
python
@patch("builtins.open", new_callable=mock_open)
def test_file_reading(mock_file):
    """使用模拟的 open 进行文件读取测试。"""
    mock_file.return_value.read.return_value = "file content"

    result = read_file("test.txt")

    mock_file.assert_called_once_with("test.txt", "r")
    assert result == "file content"
使用 Autospec
python
@patch("mypackage.DBConnection", autospec=True)
def test_autospec(db_mock):
    """使用 autospec 捕获 API 误用。"""
    db = db_mock.return_value
    db.query("SELECT * FROM users")

    # 如果 DBConnection 没有 query 方法,此处将失败
    db_mock.assert_called_once()
模拟类实例
python
class TestUserService:
    @patch("mypackage.UserRepository")
    def test_create_user(self, repo_mock):
        """使用模拟的仓库测试用户创建。"""
        repo_mock.return_value.save.return_value = User(id=1, name="Alice")

        service = UserService(repo_mock.return_value)
        user = service.create_user(name="Alice")

        assert user.name == "Alice"
        repo_mock.return_value.save.assert_called_once()
模拟属性 (Properties)
python
@pytest.fixture
def mock_config():
    """创建一个带有属性的模拟对象。"""
    config = Mock()
    type(config).debug = PropertyMock(return_value=True)
    type(config).api_key = PropertyMock(return_value="test-key")
    return config

def test_with_mock_config(mock_config):
    """测试模拟的配置属性。"""
    assert mock_config.debug is True
    assert mock_config.api_key == "test-key"

测试异步代码

使用 pytest-asyncio 进行异步测试
python
import pytest

@pytest.mark.asyncio
async def test_async_function():
    """测试异步函数。"""
    result = await async_add(2, 3)
    assert result == 5

@pytest.mark.asyncio
async def test_async_with_fixture(async_client):
    """使用异步固件进行异步测试。"""
    response = await async_client.get("/api/users")
    assert response.status_code == 200
异步固件
python
@pytest.fixture
async def async_client():
    """提供异步测试客户端的异步固件。"""
    app = create_app()
    async with app.test_client() as client:
        yield client

@pytest.mark.asyncio
async def test_api_endpoint(async_client):
    """测试使用异步固件。"""
    response = await async_client.get("/api/data")
    assert response.status_code == 200
模拟异步函数
python
@pytest.mark.asyncio
@patch("mypackage.async_api_call")
async def test_async_mock(api_call_mock):
    """使用模拟对象测试异步函数。"""
    api_call_mock.return_value = {"status": "ok"}

    result = await my_async_function()

    api_call_mock.assert_awaited_once()
    assert result["status"] == "ok"

测试异常

测试预期异常
python
def test_divide_by_zero():
    """测试除以零是否引发 ZeroDivisionError。"""
    with pytest.raises(ZeroDivisionError):
        divide(10, 0)

def test_custom_exception():
    """测试带有消息的自定义异常。"""
    with pytest.raises(ValueError, match="invalid input"):
        validate_input("invalid")
测试异常属性
python
def test_exception_with_details():
    """测试带有自定义属性的异常。"""
    with pytest.raises(CustomError) as exc_info:
        raise CustomError("error", code=400)

    assert exc_info.value.code == 400
    assert "error" in str(exc_info.value)

测试副作用

测试文件操作
python
import tempfile
import os

def test_file_processing():
    """使用临时文件测试文件处理。"""
    with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.txt') as f:
        f.write("test content")
        temp_path = f.name

    try:
        result = process_file(temp_path)
        assert result == "processed: test content"
    finally:
        os.unlink(temp_path)
使用 pytest 的 tmp_path 固件进行测试
python
def test_with_tmp_path(tmp_path):
    """使用 pytest 内置的临时路径固件进行测试。"""
    test_file = tmp_path / "test.txt"
    test_file.write_text("hello world")

    result = process_file(str(test_file))
    assert result == "hello world"
    # tmp_path 会自动清理
使用 tmpdir 固件进行测试
python
def test_with_tmpdir(tmpdir):
    """使用 pytest 的 tmpdir 固件进行测试。"""
    test_file = tmpdir.join("test.txt")
    test_file.write("data")

    result = process_file(str(test_file))
    assert result == "data"

测试组织

目录结构
tests/
├── conftest.py                 # 共享固件
├── __init__.py
├── unit/                       # 单元测试
│   ├── __init__.py
│   ├── test_models.py
│   ├── test_utils.py
│   └── test_services.py
├── integration/                # 集成测试
│   ├── __init__.py
│   ├── test_api.py
│   └── test_database.py
└── e2e/                        # 端到端测试
    ├── __init__.py
    └── test_user_flow.py
测试类
python
class TestUserService:
    """在类中组织相关的测试。"""

    @pytest.fixture(autouse=True)
    def setup(self):
        """在此类的每个测试之前运行设置。"""
        self.service = UserService()

    def test_create_user(self):
        """测试用户创建。"""
        user = self.service.create_user("Alice")
        assert user.name == "Alice"

    def test_delete_user(self):
        """测试用户删除。"""
        user = User(id=1, name="Bob")
        self.service.delete_user(user)
        assert not self.service.user_exists(1)

最佳实践

应该做 (Dos)
  • 遵循 TDD: 在编写代码之前先编写测试(红-绿-重构)
  • 只测试一件事: 每个测试应该验证单一的行为
  • 使用描述性名称: test_user_login_with_invalid_credentials_fails
  • 使用固件: 通过固件消除重复
  • 模拟外部依赖: 不要依赖外部服务
  • 测试边缘情况: 空输入、None 值、边界条件
  • 追求 80% 以上的覆盖率: 重点关注核心路径
  • 保持测试运行快速: 使用标记隔离耗时较长的测试
不该做 (Don'ts)
  • 不要测试实现细节: 测试行为而非内部细节
  • 不要在测试中使用复杂的条件语句: 保持测试简单
  • 不要忽略失败的测试: 所有测试都必须通过
  • 不要测试第三方代码: 信任库的功能是正常的
  • 不要在测试之间共享状态: 测试应该是独立的
  • 不要在测试中手动捕获异常: 使用 pytest.raises
  • 不要使用 print 语句: 使用断言和 pytest 输出
  • 不要编写过于脆弱的测试: 避免使用过度具体的模拟

常用模式

测试 API 端点 (FastAPI/Flask)
python
@pytest.fixture
def client():
    app = create_app(testing=True)
    return app.test_client()

def test_get_user(client):
    response = client.get("/api/users/1")
    assert response.status_code == 200
    assert response.json["id"] == 1

def test_create_user(client):
    response = client.post("/api/users", json={
        "name": "Alice",
        "email": "alice@example.com"
    })
    assert response.status_code == 201
    assert response.json["name"] == "Alice"
测试数据库操作
python
@pytest.fixture
def db_session():
    """创建测试数据库会话。"""
    session = Session(bind=engine)
    session.begin_nested()
    yield session
    session.rollback()
    session.close()

def test_create_user(db_session):
    user = User(name="Alice", email="alice@example.com")
    db_session.add(user)
    db_session.commit()

    retrieved = db_session.query(User).filter_by(name="Alice").first()
    assert retrieved.email == "alice@example.com"
测试类方法
python
class TestCalculator:
    @pytest.fixture
    def calculator(self):
        return Calculator()

    def test_add(self, calculator):
        assert calculator.add(2, 3) == 5

    def test_divide_by_zero(self, calculator):
        with pytest.raises(ZeroDivisionError):
            calculator.divide(10, 0)

pytest 配置

pytest.ini
ini
[pytest]
testpaths = tests
python_files = test_*.py
python_classes = Test*
python_functions = test_*
addopts =
    --strict-markers
    --disable-warnings
    --cov=mypackage
    --cov-report=term-missing
    --cov-report=html
markers =
    slow: 标记耗时较长的测试
    integration: 标记集成测试
    unit: 标记单元测试
pyproject.toml
toml
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
addopts = [
    "--strict-markers",
    "--cov=mypackage",
    "--cov-report=term-missing",
    "--cov-report=html",
]
markers = [
    "slow: 标记耗时较长的测试",
    "integration: 标记集成测试",
    "unit: 标记单元测试",
]

运行测试

bash
# 运行所有测试
pytest

# 运行特定文件
pytest tests/test_utils.py

# 运行特定测试项
pytest tests/test_utils.py::test_function

# 运行并显示详细输出
pytest -v

# 运行并检查覆盖率
pytest --cov=mypackage --cov-report=html

# 仅运行快速测试
pytest -m "not slow"

# 遇到第一个失败即停止
pytest -x

# 遇到 N 个失败后停止
pytest --maxfail=3

# 运行上次失败的测试
pytest --lf

# 按模式匹配运行测试
pytest -k "test_user"

# 失败时启动调试器
pytest --pdb

快速参考

模式用法
pytest.raises()测试预期异常
@pytest.fixture()创建可重用的测试固件
@pytest.mark.parametrize()使用多组输入运行测试
@pytest.mark.slow标记耗时较长的测试
pytest -m "not slow"跳过耗时测试
@patch()模拟函数和类
tmp_path 固件自动生成的临时目录
pytest --cov生成覆盖率报告
assert简单易读的断言

请记住: 测试也是代码。请保持它们整洁、易读且可维护。好的测试能发现 Bug,而优秀的测试能预防 Bug。

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Files

Just SKILL.md in docs/ja-JP/skills/python-testing of xu-xiang/everything-claude-code-zh.

Open the folder on GitHubat commit dfbf946

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Python Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Python Testing

What does Python Testing do?

使用 pytest、TDD 方法论、固件(Fixtures)、模拟(Mocking)、参数化及覆盖率要求的 Python 测试策略。. Python Testing is an agent skill from xu-xiang/everything-claude-code-zh.

When should I use Python Testing?

Python Testing fits situations like: tasks that involve Unit testing; tasks that involve Test-driven development.

How do I install Python Testing in Claude Code?

Run `npx skills add xu-xiang/everything-claude-code-zh --skill python-testing -a claude-code`. Or copy the skill folder (docs/ja-JP/skills/python-testing in xu-xiang/everything-claude-code-zh) into .claude/skills/python-testing in your project. Claude Code loads it when a task matches its description.

How do I install Python Testing in Codex?

Run `npx skills add xu-xiang/everything-claude-code-zh --skill python-testing -a codex`. Or copy the skill folder (docs/ja-JP/skills/python-testing in xu-xiang/everything-claude-code-zh) into .agents/skills/python-testing in your project. Codex loads it when a task matches its description.

Can I use Python Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xu-xiang/everything-claude-code-zh --skill python-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-testing, .gemini/skills/python-testing, .github/skills/python-testing and .opencode/skills/python-testing in your project.

What does Python Testing need to run?

Going by SKILL.md and its folder, Python Testing needs the command-line tools its instructions call (pytest). Our summary lists: Python 3.

Does Python Testing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Python Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Python Testing use?

Python Testing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Python Testing use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Python Testing?

Skills that share tags, products or a category with Python Testing: Python Testing (affaan-m/ECC, 274k stars), Python Testing (affaan-m/ECC, 274k stars), Agent-Core Python Testing (openJiuwen-ai/agent-core, 441 stars) and Python Testing Patterns (jh941213/my-cc-harness, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Python Testing?

xu-xiang (a GitHub user) maintains it in xu-xiang/everything-claude-code-zh, which has 1,971 GitHub stars. The repository holds 78 skills in this directory. The repository was last updated on March 5, 2026.

Source: xu-xiang/everything-claude-code-zh on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.