Agent skill

Clean Pytest

by LeoYeAI in LeoYeAI/openclaw-master-skills

Write clean, maintainable pytest tests using Fake-based testing, contract testing, and dependency injection patterns.

MITAuto-check passedTesting & QA

Install Clean Pytest

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill clean-pytest -a claude-code

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

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills clean-pytest --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/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/clean-pytest .claude/skills/clean-pytest && 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
clean-pytest
GitHub stars
2.2k
Token cost
~4.2k tokens
SKILL.md length
396 words
Files
3
Skills in repo
972
Repo updated
First seen
Licence
MIT

At a glance

Write clean, maintainable pytest tests using Fake-based testing, contract testing, and dependency injection patterns.

  • Works in 4 steps: Fakes over Mocks → Explicit AAA Pattern → Dependency Injection in Fixtures → …
  • Setting up test suites for Python/MCP projects
  • SKILL.md covers When to Use, Core Principles, Architecture Pattern and Creating Fakes, plus 4 more sections
  • Calls pytest; needs GOOGLE_APPLICATION_CREDENTIALS

What it does

Clean Pytest is an agent skill from LeoYeAI/openclaw-master-skills. Write clean, maintainable pytest tests using Fake-based testing, contract testing, and dependency injection patterns. Use when setting up test suites for Python/MCP projects, creating Fakes for external dependencies, writing contract tests, or implementing test patterns with fixtures and parametrization.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `_meta.json` and `skill.json`).

It sits in Testing & QA, covering Unit testing, Integration testing and Design patterns. It works with pytest, Model Context Protocol and Python. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Setting up test suites for Python/MCP projects
  • Creating Fakes for external dependencies
  • Writing contract tests
  • Implementing test patterns with fixtures and parametrization

Example prompts

  • “/clean-pytest”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Fakes over Mocks
  2. Explicit AAA Pattern
  3. Dependency Injection in Fixtures
  4. Contract Testing

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. 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 these keys or tokens, usually read from environment variables:

    • GOOGLE_APPLICATION_CREDENTIALS

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

Context cost

Clean Pytest loads about 4.2k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 396 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

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 LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 396 words, ~4,218 tokens.

Download SKILL.mdSave it as .claude/skills/clean-pytest/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
clean-pytest
description
Write clean, maintainable pytest tests using Fake-based testing, contract testing, and dependency injection patterns. Use when setting up test suites for Python/MCP projects, creating Fakes for external dependencies, writing contract tests, or implementing test patterns with fixtures and parametrization.
license
MIT
metadata.emoji
🧪
metadata.homepage
https://github.com/numinstante/skills
metadata.os
darwin, linux, windows
metadata.tags
python, pytest, testing, tdd, mcp, contract-testing

Clean Pytest

Clean, maintainable pytest test patterns using Fake-based testing, contract testing, and dependency injection. Focuses on test isolation, reusability, and clarity through explicit AAA pattern and well-structured fixtures.

When to Use

  • Setting up test suites for Python/MCP projects
  • Creating Fake implementations for external dependencies
  • Writing contract tests for MCP tools/controllers
  • Implementing test patterns with dependency injection
  • Testing layered architectures (Controllers → Services → Repositories)
  • Writing parametrized tests for multiple scenarios

Core Principles

1. Fakes over Mocks

Use Fake classes instead of mocking with unittest.mock. Fakes are in-memory implementations that mimic real dependencies without external calls.

Why Fakes?

  • More readable and maintainable
  • Easier to debug
  • Better test isolation
  • No monkey-patching magic
  • Self-documenting behavior
2. Explicit AAA Pattern

Structure every test into three clear phases with comments:

python
# Arrange
# Set up test data and dependencies

# Act
# Execute the code under test

# Assert
# Verify the result
3. Dependency Injection in Fixtures

Inject dependencies between fixtures to maintain relationships and avoid duplication.

4. Contract Testing

Verify that components register tools/functions correctly and pass expected arguments.

Architecture Pattern

Controller (MCP Tools)
    ↓
Service (Business Logic)
    ↓
Repository (Data Access)
    ↓
Fake (Test Implementation)

Creating Fakes

Basic Fake Structure

Create a Fake class that implements the same interface as the real dependency:

python
# tests/fakes.py
from typing import Any, Dict, List, Optional

class FakeAuth:
    """Fake implementation of AuthProvider for testing."""
    def __init__(self) -> None:
        self.created: List[Dict[str, Any]] = []
        self.deleted: List[str] = []
        self._seq = 0
        self.fail_on_create: bool = False

    def create_user(self, email: str, password: str, display_name: str) -> str:
        if self.fail_on_create:
            raise RuntimeError("create_user failed (fake)")
        self._seq += 1
        uid = f"uid-{self._seq}"
        rec = {"uid": uid, "email": email, "display_name": display_name}
        self.created.append(rec)
        return uid

    def delete_user(self, uid: str) -> None:
        self.deleted.append(uid)
Repository Fake
python
class FakeUsersRepo:
    """Fake implementation of UsersRepository."""
    def __init__(self) -> None:
        self.users: Dict[str, Dict[str, Any]] = {}
        self.fail_on_upsert: bool = False

    def upsert_user_doc(self, uid: str, data: Dict[str, Any]) -> None:
        if self.fail_on_upsert:
            raise RuntimeError("upsert_user_doc failed (fake)")
        self.users[uid] = dict(data)

    def list_users(self, limit: Optional[int] = None) -> List[Dict[str, Any]]:
        items = list(self.users.values())
        if limit and limit > 0:
            items = items[:limit]
        return [dict(it) for it in items]
Controlled Failure Fakes
python
class FakeAuth:
    def __init__(self) -> None:
        self.fail_on_create: bool = False  # Control failure in tests

    def create_user(self, email: str, password: str, display_name: str) -> str:
        if self.fail_on_create:
            raise RuntimeError("create_user failed (fake)")
        # ... rest of implementation
Nested Repository Fakes
python
class FakeSectorsRepo:
    def __init__(self, institutions: FakeInstitutionsRepo | None = None) -> None:
        self.institutions = institutions  # Inject dependency
        self.data: Dict[str, Dict[str, Dict[str, Any]]] = {}

    def institution_exists(self, institution_id: str) -> bool:
        return bool(self.institutions and institution_id in self.institutions.data)

    def upsert_sector(self, institution_id: str, sector_id: str, data: Dict[str, Any]) -> None:
        self.data.setdefault(institution_id, {})[sector_id] = dict(data)

Fixtures

Basic Fixture (conftest.py)
python
# tests/conftest.py
import pytest
from tests.fakes import FakeAuth, FakeUsersRepo

@pytest.fixture()
def fake_auth():
    """Provide a fresh FakeAuth for each test."""
    return FakeAuth()

@pytest.fixture()
def fake_users_repo():
    """Provide a fresh FakeUsersRepo for each test."""
    return FakeUsersRepo()
Fixture with Dependency Injection
python
@pytest.fixture()
def fake_sectors_repo(fake_institutions_repo):
    """FakeSectorsRepo depends on FakeInstitutionsRepo."""
    return FakeSectorsRepo(institutions=fake_institutions_repo)

@pytest.fixture()
def fake_rooms_repo(fake_sectors_repo):
    """FakeRoomsRepo depends on FakeSectorsRepo."""
    return FakeRoomsRepo(sectors=fake_sectors_repo)
Environment Fixture
python
@pytest.fixture()
def user_env(fake_auth, fake_users_repo):
    """Provide service and all dependencies for user operations."""
    from myapp.services.user_service import UserService
    svc = UserService(fake_auth, fake_users_repo)
    return svc, fake_auth, fake_users_repo
Seeded Environment Fixture
python
@pytest.fixture()
def user_env_seeded(user_env):
    """Environment with pre-seeded data."""
    svc, auth, repo = user_env
    svc.add_user(email="test@example.com", password="secret", name="Test User")
    return svc
Fixture with Cleanup
python
@pytest.fixture()
def temp_file():
    """Provide a temporary file and clean up after test."""
    import tempfile
    import os
    fd, path = tempfile.mkstemp()
    os.close(fd)
    yield path
    os.unlink(path)

Service Layer Testing

Basic AAA Pattern Test
python
# tests/test_user_service.py
import pytest
from myapp.services.user_service import UserService

def test_add_user_success(fake_auth, fake_users_repo):
    # Arrange
    svc = UserService(fake_auth, fake_users_repo)
    email = "test@example.com"
    password = "secret"
    name = "Test User"

    # Act
    result = svc.add_user(email=email, password=password, name=name)

    # Assert
    assert result["status"] == "ok"
    assert result["user"]["email"] == email
    assert result["user"]["name"] == name
    assert result["uid"] in fake_users_repo.users
Parametrized Tests
python
@pytest.mark.parametrize(
    "email,password,name,role",
    [
        ("a@example.com", "secret", "Alice", "admin"),
        ("b@example.com", "p@ss", "Bob", "user"),
    ],
)
def test_add_user_parametrized(user_env, email, password, name, role):
    svc, _auth, _repo = user_env

    # Act
    res = svc.add_user(email=email, password=password, name=name, global_role=role)

    # Assert
    assert res["status"] == "ok"
    assert res["user"]["email"] == email
    assert res["user"]["name"] == name
    assert res["user"]["globalRole"] == role
Testing Error Scenarios with Fakes
python
@pytest.mark.parametrize("email", ["c@example.com", "d@example.com"])
def test_add_user_rollback_on_firestore_failure(fake_auth, fake_users_repo, email):
    # Arrange
    fake_users_repo.fail_on_upsert = True
    svc = UserService(fake_auth, fake_users_repo)

    # Act & Assert
    with pytest.raises(RuntimeError):
        svc.add_user(email=email, password="secret", name="Bob")

    # Assert rollback
    assert fake_auth.deleted, "Expected auth user to be deleted on Firestore failure"
Testing Timestamp Normalization
python
def test_list_users_normalizes_timestamps_to_iso(user_env):
    # Arrange
    svc, _auth, repo = user_env
    from datetime import datetime
    repo.users["u1"] = {
        "id": "u1",
        "email": "x@y.z",
        "name": "X",
        "globalRole": "user",
        "createdAt": datetime(2024, 1, 1),
        "updatedAt": datetime(2024, 1, 2),
    }

    # Act
    res = svc.list_users(limit=10)

    # Assert
    assert res["status"] == "ok"
    assert res["count"] == 1
    user = res["users"][0]
    assert isinstance(user["createdAt"], str)
    assert isinstance(user["updatedAt"], str)

Contract Testing

MCP Tool Registration Contract

Test that controllers properly register tools with expected signatures:

python
# tests/test_controllers_contract.py
from typing import Any, Callable, Dict

class FakeMCP:
    """Minimal FakeMCP for contract testing."""
    def __init__(self) -> None:
        self.tools: Dict[str, Callable[..., Any]] = {}
        self.meta: Dict[str, Dict[str, Any]] = {}

    def tool(self, name: str, description: str, tags: Optional[set] = None, meta: Optional[dict] = None):
        def decorator(fn: Callable[..., Any]):
            self.tools[name] = fn
            self.meta[name] = {
                "description": description,
                "tags": set(tags or set()),
                "meta": dict(meta or {}),
            }
            return fn
        return decorator


class FakeUserService:
    """Simple fake service that records calls."""
    def __init__(self):
        self.calls = []

    def add_user(self, **kwargs):
        self.calls.append(("add_user", kwargs))
        return {"status": "ok", "op": "add_user", "args": kwargs}


def test_users_controller_contract():
    # Arrange
    from myapp.controllers.users_controller import UsersController
    fake = FakeMCP()
    svc = FakeUserService()
    UsersController(fake, svc)

    # Assert tool registration
    assert "add_user" in fake.tools
    assert "list_users" in fake.tools

    # Act & Assert tool behavior
    res = fake.tools["add_user"](
        email="a@x.y", password="s3cr3t", name="Alice", global_role="admin"
    )
    assert res["status"] == "ok"
    assert res["op"] == "add_user"
    assert res["args"]["email"] == "a@x.y"
Parametrized Contract Tests
python
@pytest.mark.parametrize(
    "email,password,name,role",
    [
        ("a@x.y", "s3cr3t", "Alice", "admin"),
        ("b@x.y", "p@ssw0rd", "Bob", "user"),
    ],
)
def test_users_add_user_parametrized(_users_env, email, password, name, role):
    # Arrange
    fake, _ = _users_env

    # Act
    res = fake.tools["add_user"](
        email=email, password=password, name=name, global_role=role
    )

    # Assert
    assert res["status"] == "ok"
    assert res["op"] == "add_user"
    assert res["args"]["email"] == email

Repository Layer Testing

Testing Repository Operations
python
@pytest.fixture()
def repo_env(fake_institutions_repo, fake_sectors_repo):
    # Seed data
    fake_institutions_repo.upsert("inst1", {"id": "inst1", "name": "Inst One"})
    fake_sectors_repo.upsert_sector(
        "inst1", "er", {"id": "er", "name": "ER", "slug": "er", "isActive": True}
    )
    return fake_sectors_repo
Show full SKILL.md (159 more words)Show less
Testing Multiple Data Scenarios
python
@pytest.mark.parametrize("rooms", [
    ["101"],
    ["201", {"name": "102", "id": "room-102"}],
])
def test_add_and_list_rooms(room_env, rooms):
    svc, _ = room_env

    # Act
    res = svc.add_sector_rooms("inst1", "er", rooms)

    # Assert
    assert res["status"] == "ok"
    assert res["count"] == len(rooms)

    lst = svc.list_sector_rooms("inst1", "er", limit=10)
    assert lst["status"] == "ok"
    assert lst["count"] == len(rooms)
Testing Limit Behavior
python
@pytest.mark.parametrize("limit", [1, 3])
def test_list_rooms_limits(room_env_seeded, limit):
    svc = room_env_seeded

    # Act
    lst = svc.list_sector_rooms("inst1", "er", limit=limit)

    # Assert
    assert lst["status"] == "ok"
    assert lst["count"] == min(2, limit)  # 2 items seeded
Testing Not Found Scenarios
python
@pytest.mark.parametrize("room_id,deleted", [
    ("room-102", True),
    ("room-999", False),
])
def test_remove_rooms_parametrized(room_env_seeded, room_id, deleted):
    svc = room_env_seeded

    # Act
    res = svc.remove_sector_room("inst1", "er", room_id)

    # Assert
    assert res["deleted"] is deleted
    if not deleted:
        assert res.get("reason") == "room_not_found"

Integration Testing

Conditional Integration Tests

Skip integration tests when external dependencies are not available:

python
# tests/test_integration_wiring.py
import os
import pytest

# Gate this integration test on presence of credentials
_ENV_KEYS = (
    "FIREBASE_SERVICE_ACCOUNT",
    "GOOGLE_APPLICATION_CREDENTIALS",
)
_has_env_creds = any(os.getenv(k) for k in _ENV_KEYS)

pytestmark = [
    pytest.mark.integration,
    pytest.mark.skipif(
        not _has_env_creds,
        reason=(
            "Integration test requires Firebase Admin credentials via env "
            "(FIREBASE_SERVICE_ACCOUNT or GOOGLE_APPLICATION_CREDENTIALS)"
        ),
    ),
]

@pytest.mark.integration
def test_build_app_initializes_and_registers_tools():
    # Arrange
    from myapp.wiring import build_app

    # Act
    app = build_app()

    # Assert
    assert hasattr(app, "run")
Test Isolation

Each test should be independent and not share state:

python
def test_user_created_in_one_test_not_visible_in_another(fake_auth, fake_users_repo):
    # Arrange
    svc1 = UserService(fake_auth, fake_users_repo)

    # Act
    result1 = svc1.add_user(email="test1@example.com", password="secret", name="User1")

    # Assert - second test with fresh fixtures should not see this user
    svc2 = UserService(fake_auth, fake_users_repo)
    users = svc2.list_users()
    assert users["count"] == 1  # Only the user from this test

Testing Anti-Patterns to Avoid

Don't Mock What You Don't Own

❌ Bad - Mocking external library:

python
@patch('firebase_admin.auth.create_user')
def test_add_user(mock_create_user):
    mock_create_user.return_value = Mock(uid="uid-1")
    # ... test code

✅ Good - Use Fake for your interface:

python
def test_add_user(fake_auth, fake_users_repo):
    svc = UserService(fake_auth, fake_users_repo)
    # ... test code
Don't Test Implementation Details

❌ Bad - Testing internal method calls:

python
def test_add_user(fake_auth, fake_users_repo):
    svc = UserService(fake_auth, fake_users_repo)
    svc.add_user(email="test@example.com", password="secret", name="User")
    assert fake_auth.created == [{"uid": "uid-1", ...}]  # Implementation detail

✅ Good - Testing observable behavior:

python
def test_add_user(fake_auth, fake_users_repo):
    svc = UserService(fake_auth, fake_users_repo)
    result = svc.add_user(email="test@example.com", password="secret", name="User")
    assert result["status"] == "ok"
    assert result["user"]["email"] == "test@example.com"
Don't Skip Error Paths

❌ Bad - Only happy path:

python
def test_add_user_success(fake_auth, fake_users_repo):
    # Only tests success case

✅ Good - Test all scenarios:

python
def test_add_user_success(fake_auth, fake_users_repo):
    # Happy path

def test_add_user_rollback_on_firestore_failure(fake_auth, fake_users_repo):
    # Error path

def test_add_user_handles_duplicate_email(fake_auth, fake_users_repo):
    # Edge case

Running Tests

bash
# Run all tests
pytest

# Run with coverage
pytest --cov=myapp --cov-report=term-missing

# Run specific test file
pytest tests/test_user_service.py

# Run specific test
pytest tests/test_user_service.py::test_add_user_success

# Run parametrized tests with verbose output
pytest -v tests/test_user_service.py::test_add_user_parametrized

# Skip integration tests
pytest -m "not integration"

# Run only integration tests
pytest -m integration

# Stop on first failure
pytest -x

# Show local variables on failure
pytest -l

# Run tests in parallel (with pytest-xdist)
pytest -n auto

Best Practices Checklist

  • Use Fake classes instead of unittest.mock
  • Structure tests with explicit AAA comments
  • Use fixtures for test setup
  • Inject dependencies between fixtures
  • Parametrize tests for multiple scenarios
  • Test happy paths and error paths
  • Test edge cases and boundaries
  • Write contract tests for interfaces
  • Ensure test isolation
  • Use descriptive test names
  • Keep tests focused on one behavior
  • Avoid testing implementation details
  • Test at appropriate level (unit vs integration)
  • Mock external dependencies appropriately
  • Maintain test coverage

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/clean-pytest of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • skill.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Clean Pytest 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.

Clean Pytest compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Clean Pytest this skillLeoYeAI/openclaw-master-skills2.2k—~4.2kAutomated safety check: PassMIT
Python Testingathola/claude-night-market342—~800Automated safety check: PassMIT
Adk Verify Snippetsgoogle/adk-python22k—~1.4kAutomated safety check: PassApache-2.0
Hermetic Python Unit TestsdimensionalOS/dimos4.6k—~1.4kAutomated safety check: PassCustom licence
Test Coverage Reviewareed1192/finance-news-aggregator149—~2.6kAutomated safety check: PassMIT
JS-in-HTML Testingliaohch3/claude-tap3.3k—~924Automated safety check: PassMIT

Similar skills

  • Python Testing

    athola/claude-night-market

    Python testing patterns with pytest, fixtures, TDD, mocking, async and integration tests.

    342 GitHub stars~800 tokensUpdated 2 days ago
    Testing & QAAuto-check passed
  • Adk Verify Snippets

    google/adk-python

    Official

    Checks that every Python code block in a Markdown file actually compiles and runs, by extracting each block to a temporary file, executing it in an isolated subprocess, and writing a pass/fail…

    22k GitHub stars~1.4k tokensUpdated yesterday
    Testing & QAAuto-check passed
  • Hermetic Python Unit Tests

    dimensionalOS/dimos

    Rules for writing, fixing and reviewing pytest unit tests that are hermetic: behavior-focused, deterministic, isolated and cheap to run.

    4.6k GitHub stars~1.4k tokensUpdated today
    Testing & QAAuto-check passed
  • Test Coverage Review

    areed1192/finance-news-aggregator

    Audit, plan, write, and verify unit tests for Python projects using pytest.

    149 GitHub stars~2.6k tokensUpdated 5 mo ago
    Testing & QAAuto-check passed
  • JS-in-HTML Testing

    liaohch3/claude-tap

    Tests JavaScript embedded in an HTML file in two layers: pytest checks of the logic ported to Python, and Playwright runs in a real browser for the DOM.

    3.3k GitHub stars~924 tokensUpdated 16 days ago
    Testing & QAAuto-check passed
  • Write Test

    389ds/389-ds-base

    Add or extend a pytest integration test for 389 Directory Server under dirsrvtests/.

    294 GitHub stars~1.8k tokensUpdated yesterday
    Testing & QAAuto-check passed

More from LeoYeAI/openclaw-master-skills

All 972 skills in this repo
  • DevOps Pipeline Management

    LeoYeAI/openclaw-master-skills

    Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.

    2.2k GitHub stars~4.2k tokensUpdated 2 mo ago
    Auto-check: notes
  • Feishu Document Collaboration

    LeoYeAI/openclaw-master-skills

    Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.

    2.2k GitHub stars~2k tokensUpdated 2 mo ago
    Auto-check passed
  • Files Memory System

    LeoYeAI/openclaw-master-skills

    Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.

    2.2k GitHub stars~3.8k tokensUpdated 2 mo ago
    Auto-check passed
  • GEO-Claw AI Visibility Agent

    LeoYeAI/openclaw-master-skills

    Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.

    2.2k GitHub stars~4.7k tokensUpdated 2 mo ago
    Auto-check passed
  • Google Workspace CLI

    LeoYeAI/openclaw-master-skills

    Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.

    2.2k GitHub stars~2.6k tokensUpdated 2 mo ago
    Auto-check: notes
  • HealthFit Health Advisors

    LeoYeAI/openclaw-master-skills

    Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.

    2.2k GitHub stars~4.4k tokensUpdated 2 mo ago
    Auto-check passed

Categories

Questions about Clean Pytest

What does Clean Pytest do?

Write clean, maintainable pytest tests using Fake-based testing, contract testing, and dependency injection patterns. Clean Pytest is an agent skill from LeoYeAI/openclaw-master-skills. Write clean, maintainable pytest tests using Fake-based testing, contract testing, and dependency injection patterns.

When should I use Clean Pytest?

Clean Pytest fits situations like: setting up test suites for Python/MCP projects; creating Fakes for external dependencies; writing contract tests; implementing test patterns with fixtures and parametrization.

How do I install Clean Pytest in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill clean-pytest -a claude-code`. Or copy the skill folder (skills/clean-pytest in LeoYeAI/openclaw-master-skills) into .claude/skills/clean-pytest in your project. Claude Code loads it when a task matches its description.

How do I install Clean Pytest in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill clean-pytest -a codex`. Or copy the skill folder (skills/clean-pytest in LeoYeAI/openclaw-master-skills) into .agents/skills/clean-pytest in your project. Codex loads it when a task matches its description.

Can I use Clean Pytest 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 LeoYeAI/openclaw-master-skills --skill clean-pytest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/clean-pytest, .gemini/skills/clean-pytest, .github/skills/clean-pytest and .opencode/skills/clean-pytest in your project.

What does Clean Pytest need to run?

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

Does Clean Pytest 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 Clean Pytest 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 Clean Pytest use?

Clean Pytest is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Clean Pytest use?

About 4.2k tokens (SKILL.md is roughly 17k 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 Clean Pytest?

Skills that share tags, products or a category with Clean Pytest: Python Testing (athola/claude-night-market, 342 stars), Adk Verify Snippets (google/adk-python, 22k stars), Hermetic Python Unit Tests (dimensionalOS/dimos, 4.6k stars) and Test Coverage Review (areed1192/finance-news-aggregator, 149 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Clean Pytest?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,159 GitHub stars. The repository holds 972 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.