testing

A skill that provides guidance for planning tests, writing tests, and setting up testing systems. Testing checks that code behaves as expected; TDD means writing tests before the code they verify.

In plain words
What is it for?
Use it when designing test strategies, adding unit, integration, contract, or end-to-end tests, or building testing infrastructure.
Why use it?
It helps choose suitable levels of testing and structure tests consistently across a project.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/atstaeff/ai-agents/testing
Any agent
npx skills add atstaeff/ai-agents --skill testing
Clone the repo
git clone --depth 1 https://github.com/atstaeff/ai-agents

Made for: Claude Code, Codex.

Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,433 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.01433
Opus 5 $0.00009 $0.00717
Sonnet 5 $0.00003 $0.00287
Haiku 4.5 $0.00002 $0.00143

Measured 2d ago against content hash b1c5af659396, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

testing scanned grade A with 0 findings 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/testing/SKILL.md · 227 lines

How it starts

The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Testing Patterns Skill

Instructions for AI

Apply comprehensive testing best practices. Use this skill when designing test strategies, writing tests, or setting up testing infrastructure.

Test Pyramid

         /\
        / E2E \          ~5%  — Critical user journeys
       /--------\
      / Contract  \      ~10% — API contracts
     /--------------\
    /  Integration    \  ~25% — DB, queues, external APIs
   /--------------------\
  /     Unit Tests       \ ~60% — Business logic, pure functions
 /------------------------\

Unit Testing Patterns

Arrange-Act-Assert

class TestPriceCalculator:
    def test_applies_percentage_discount(self):
        # Arrange
        calculator = PriceCalculator()
        product = Product(price=100.0)
        discount = PercentageDiscount(rate=0.2)

        # Act
        result = calculator.calculate(product, discount)

        # Assert
        assert result == 80.0

Parameterized Tests

import pytest

@pytest.mark.parametrize("input_price,discount_rate,expected", [
    (100.0, 0.0, 100.0),
    (100.0, 0.1, 90.0),
    (100.0, 0.5, 50.0),
    (100.0, 1.0, 0.0),
    (0.0, 0.5, 0.0),
])
def test_discount_calculation(input_price, discount_rate, expected):
    result = calculate_discount(input_price, discount_rate)
    assert result == expected

Fakes over Mocks

class FakeEmailSender:
    """In-memory fake — captures sent emails for verification."""

    def __init__(self):
        self.sent_emails: list[Email] = []

    async def send(self, email: Email) -> None:
        self.sent_emails.append(email)

# In tests
async def test_order_confirmation_sends_email(fake_email_sender):
    service = OrderService(email_sender=fake_email_sender)
    await service.place_order(sample_order)
    
    assert len(fake_email_sender.sent_emails) == 1
    assert fake_email_sender.sent_emails[0].subject == "Order Confirmation"

Factory Pattern for Test Data

from polyfactory.factories.pydantic_factory import ModelFactory

class UserFactory(ModelFactory):
    __model__ = User
    
    @classmethod
    def admin(cls) -> User:
        return cls.build(role=UserRole.ADMIN)
    
    @classmethod
    def with_orders(cls, count: int = 3) -> User:
        user = cls.build()
        user.orders = OrderFactory.batch(count)
        return user

Read the full file on GitHub · 227 lines

Changes

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.

  1. 2d ago First seen · 227 lines · 17 tokens per session scan A b1c5af659396

Subscribe to this mod's changes

testing is a skill published in the GitHub repository atstaeff/ai-agents (2 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 1,433 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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