Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dmitriyyukhanov/claude-plugins/python-testingnpx skills add DmitriyYukhanov/claude-plugins --skill python-testinggit clone --depth 1 https://github.com/DmitriyYukhanov/claude-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.01148 |
| Opus 5 | $0.00014 | $0.00574 |
| Sonnet 5 | $0.00006 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
python-testing scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
with patch("myapp.services.requests.get") as mock_get: How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Testing Skill
You are a testing specialist for Python projects.
Testing Framework
Framework Detection
conftest.pyorpytest.ini→ pytest[tool.pytest.ini_options]inpyproject.toml→ pytestunittestimports → unittest (suggest migrating to pytest)tox.ini→ tox runnernox→ nox runner
Test Distribution
- ~75% Unit Tests: Fast, mocked dependencies
- ~20% Integration Tests: Database, API interactions
- ~5% E2E Tests: Full workflows
Unit Test Patterns
Arrange-Act-Assert with Fixtures
import pytest
from unittest.mock import Mock, AsyncMock, patch
class TestUserService:
@pytest.fixture
def mock_repository(self) -> Mock:
return Mock(spec=UserRepository)
@pytest.fixture
def service(self, mock_repository: Mock) -> UserService:
return UserService(mock_repository)
def test_get_user_returns_user_when_exists(
self, service: UserService, mock_repository: Mock
) -> None:
# Arrange
expected_user = User(id="1", name="Test", email="[email protected]")
mock_repository.find_by_id.return_value = expected_user
# Act
result = service.get_user("1")
# Assert
assert result == expected_user
mock_repository.find_by_id.assert_called_once_with("1")
def test_get_user_returns_none_when_not_exists(
self, service: UserService, mock_repository: Mock
) -> None:
# Arrange
mock_repository.find_by_id.return_value = None
# Act
result = service.get_user("unknown")
# Assert
assert result is None
Parametrized Tests
@pytest.mark.parametrize("input_value,expected", [
("hello", "HELLO"),
("", ""),
("Hello World", "HELLO WORLD"),
])
def test_to_uppercase(input_value: str, expected: str) -> None:
assert to_uppercase(input_value) == expected
Mocking Strategies
# Mock with spec for type safety
mock_repo = Mock(spec=UserRepository)
# Patch module-level dependencies
with patch("myapp.services.requests.get") as mock_get:
mock_get.return_value.json.return_value = {"id": "1"}
result = fetch_user("1")
# AsyncMock for async functions
mock_client = AsyncMock(spec=HttpClient)
mock_client.get.return_value = {"data": "value"}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 181 lines · 28 tokens per session scan A 8704ca4da2e4
python-testing is a skill published in the GitHub repository DmitriyYukhanov/claude-plugins (7 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 1,148 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
dummy-dataset
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
outcome-roadmap
Transform an output-focused roadmap into an outcome-focused one that communicates strategic intent. Rewrites initiatives as outcome statements reflecting user and business impacts. Use when shifting to outcome roadmaps, making a roadmap more strategic, or rewriting feature lists as outcomes.
retro
Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.
release-notes
Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.
shipping-artifacts
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded…
ideal-customer-profile
Identify the Ideal Customer Profile (ICP) from research data with demographics, behaviors, JTBD, and needs. Use when defining your ICP, analyzing PMF survey data, or understanding who your best customers are.