tdd-pytest
Python/pytest TDD specialist for test-driven development workflows. Use when writing tests, auditing test quality, running pytest, or generating test reports. Integrates with uv and pyproject.toml configuration.
Best use case
tdd-pytest is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Python/pytest TDD specialist for test-driven development workflows. Use when writing tests, auditing test quality, running pytest, or generating test reports. Integrates with uv and pyproject.toml configuration.
Python/pytest TDD specialist for test-driven development workflows. Use when writing tests, auditing test quality, running pytest, or generating test reports. Integrates with uv and pyproject.toml configuration.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "tdd-pytest" skill to help with this workflow task. Context: Python/pytest TDD specialist for test-driven development workflows. Use when writing tests, auditing test quality, running pytest, or generating test reports. Integrates with uv and pyproject.toml configuration.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tdd-pytest/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tdd-pytest Compares
| Feature / Agent | tdd-pytest | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Python/pytest TDD specialist for test-driven development workflows. Use when writing tests, auditing test quality, running pytest, or generating test reports. Integrates with uv and pyproject.toml configuration.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# TDD-Pytest Skill
Activate this skill when the user needs help with:
- Writing tests using TDD methodology (Red-Green-Refactor)
- Auditing existing pytest test files for quality
- Running tests with coverage
- Generating test reports to `TESTING_REPORT.local.md`
- Setting up pytest configuration in `pyproject.toml`
## TDD Workflow
### Red-Green-Refactor Cycle
1. **RED** - Write a failing test first
- Test should fail for the right reason (not import errors)
- Test should be minimal and focused
- Show the failing test output
2. **GREEN** - Write minimal code to pass
- Only implement what's needed to pass the test
- No premature optimization
- Show the passing test output
3. **REFACTOR** - Improve code while keeping tests green
- Clean up duplication
- Improve naming
- Extract functions/classes if needed
- Run tests after each change
## Test Organization
### File Structure
```text
project/
src/
module.py
tests/
conftest.py # Shared fixtures
test_module.py # Tests for module.py
pyproject.toml # Pytest configuration
```
### Naming Conventions
- Test files: `test_*.py` or `*_test.py`
- Test functions: `test_*`
- Test classes: `Test*`
- Fixtures: Descriptive names (`mock_database`, `sample_user`)
## Pytest Best Practices
### Fixtures
```python
import pytest
@pytest.fixture
def sample_config():
return {"key": "value"}
@pytest.fixture
def mock_client(mocker):
return mocker.MagicMock()
```
### Parametrization
```python
@pytest.mark.parametrize("input,expected", [
("hello", "HELLO"),
("world", "WORLD"),
("", ""),
])
def test_uppercase(input, expected):
assert input.upper() == expected
```
### Async Tests
```python
import pytest
@pytest.mark.asyncio
async def test_async_function():
result = await async_operation()
assert result == expected
```
### Exception Testing
```python
def test_raises_value_error():
with pytest.raises(ValueError, match="invalid input"):
process_input(None)
```
## Running Tests
### With uv
```bash
uv run pytest # Run all tests
uv run pytest tests/test_module.py # Run specific file
uv run pytest -k "test_name" # Run by name pattern
uv run pytest -v --tb=short # Verbose with short traceback
uv run pytest --cov=src --cov-report=term # With coverage
```
### Common Flags
- `-v` / `--verbose` - Detailed output
- `-x` / `--exitfirst` - Stop on first failure
- `--tb=short` - Short tracebacks
- `--tb=no` - No tracebacks
- `-k EXPR` - Run tests matching expression
- `-m MARKER` - Run tests with marker
- `--cov=PATH` - Coverage for path
- `--cov-report=term-missing` - Show missing lines
## pyproject.toml Configuration
### Minimal Setup
```toml
[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]
```
### Full Configuration
```toml
[tool.pytest.ini_options]
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
testpaths = ["tests"]
python_files = ["test_*.py", "*_test.py"]
python_functions = ["test_*"]
python_classes = ["Test*"]
addopts = "-v --tb=short"
markers = [
"slow: marks tests as slow",
"integration: marks integration tests",
]
filterwarnings = [
"ignore::DeprecationWarning",
]
[tool.coverage.run]
source = ["src"]
branch = true
omit = ["tests/*", "*/__init__.py"]
[tool.coverage.report]
exclude_lines = [
"pragma: no cover",
"if TYPE_CHECKING:",
"raise NotImplementedError",
]
fail_under = 80
show_missing = true
```
## Report Generation
The `TESTING_REPORT.local.md` file should contain:
1. Test execution summary (passed/failed/skipped)
2. Coverage metrics by module
3. Audit findings by severity
4. Recommendations with file:line references
5. Evidence (command outputs)
## Integration with Conversation
When the user asks to write tests:
1. Check conversation history for context about what to test
2. Identify the code/feature being discussed
3. If unclear, ask clarifying questions:
- "What specific behavior should I test?"
- "Should I include edge cases for X?"
- "Do you want unit tests, integration tests, or both?"
4. Follow TDD: Write failing test first, then implement
## Commands Available
- `/tdd-pytest:init` - Initialize pytest configuration
- `/tdd-pytest:test [path]` - Write tests using TDD (context-aware)
- `/tdd-pytest:test-all` - Run all tests
- `/tdd-pytest:report` - Generate/update TESTING_REPORT.local.mdRelated Skills
pytest
Python testing framework for writing simple, scalable, and powerful tests
pytest-recording
Work with pytest-recording (VCR.py) for recording and replaying HTTP interactions in tests. Use when writing VCR tests, managing cassettes, configuring VCR options, filtering sensitive data, or debugging recorded HTTP responses.
pytest-mock-guide
Guide for using pytest-mock plugin to write tests with mocking. Use when writing pytest tests that need mocking, patching, spying, or stubbing. Covers mocker fixture usage, patch methods, spy/stub patterns, and assertion helpers.
pytest-mastery
Python testing with pytest using uv package manager. Use when: (1) Running Python tests, (2) Writing test files or test functions, (3) Setting up fixtures, (4) Parametrizing tests, (5) Generating coverage reports, (6) Testing FastAPI applications, (7) Debugging test failures, (8) Configuring pytest options. Triggers: "run tests", "write tests", "test coverage", "pytest", "unit test", "integration test", "test FastAPI".
python-pytest-patterns
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
azure-quotas
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".
raindrop-io
Manage Raindrop.io bookmarks with AI assistance. Save and organize bookmarks, search your collection, manage reading lists, and organize research materials. Use when working with bookmarks, web research, reading lists, or when user mentions Raindrop.io.
zlibrary-to-notebooklm
自动从 Z-Library 下载书籍并上传到 Google NotebookLM。支持 PDF/EPUB 格式,自动转换,一键创建知识库。
discover-skills
当你发现当前可用的技能都不够合适(或用户明确要求你寻找技能)时使用。本技能会基于任务目标和约束,给出一份精简的候选技能清单,帮助你选出最适配当前任务的技能。
web-performance-seo
Fix PageSpeed Insights/Lighthouse accessibility "!" errors caused by contrast audit failures (CSS filters, OKLCH/OKLAB, low opacity, gradient text, image backgrounds). Use for accessibility-driven SEO/performance debugging and remediation.
project-to-obsidian
将代码项目转换为 Obsidian 知识库。当用户提到 obsidian、项目文档、知识库、分析项目、转换项目 时激活。 【激活后必须执行】: 1. 先完整阅读本 SKILL.md 文件 2. 理解 AI 写入规则(默认到 00_Inbox/AI/、追加式、统一 Schema) 3. 执行 STEP 0: 使用 AskUserQuestion 询问用户确认 4. 用户确认后才开始 STEP 1 项目扫描 5. 严格按 STEP 0 → 1 → 2 → 3 → 4 顺序执行 【禁止行为】: - 禁止不读 SKILL.md 就开始分析项目 - 禁止跳过 STEP 0 用户确认 - 禁止直接在 30_Resources 创建(先到 00_Inbox/AI/) - 禁止自作主张决定输出位置
obsidian-helper
Obsidian 智能笔记助手。当用户提到 obsidian、日记、笔记、知识库、capture、review 时激活。 【激活后必须执行】: 1. 先完整阅读本 SKILL.md 文件 2. 理解 AI 写入三条硬规矩(00_Inbox/AI/、追加式、白名单字段) 3. 按 STEP 0 → STEP 1 → ... 顺序执行 4. 不要跳过任何步骤,不要自作主张 【禁止行为】: - 禁止不读 SKILL.md 就开始工作 - 禁止跳过用户确认步骤 - 禁止在非 00_Inbox/AI/ 位置创建新笔记(除非用户明确指定)