Testing logo

Testing

Community
mcouthon
testing

Behavioral testing strategy — deciding what to test and how. Use when writing tests, reviewing test quality, or fixing tests that test mocks instead of behavior. Triggers on: 'use testing mode', 'write tests', 'test strategy', 'tests are brittle', 'tests test mocks', 'improve test quality', 'what should I test'. Full access mode - can write and run tests.

Overview

Publishermcouthon
Repositoryagents
Skill nametesting
Stars
79
Forks
11
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mcouthon on GitHub. Read the source before you install it.

Installation

Install the Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mcouthon/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/generated/claude/skills/testing .claude/skills/testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Testing Strategy

Decide what to test and how to test it. Write tests that catch real regressions.

"Tests should be coupled to the behavior of code and decoupled from the structure of code." — Kent Beck

The Self-Test

Ask these 5 questions about EVERY test you write. If any answer is "no", rewrite the test.

#QuestionWhat It Checks
1Could I rewrite the internals and this test still passes?Structure-insensitive
2Am I testing what the code SHOULD DO, not what it DOES?Behavioral
3If this test passes, do I trust the code works?Predictive / Inspiring
4Am I testing through the public API?Public contract
5Am I checking state/output, not verifying call sequences?State > Interaction

The Refactoring Litmus Test: After writing a test, imagine completely rewriting the internals while keeping the same public behavior. Would the test still pass? If not, it's coupled to structure and will become a maintenance burden.

Test Double Decision Tree

Choose the simplest test double that gives confidence. Work top to bottom — stop at the first "yes".

Can I use the REAL implementation?
├─ Yes → Use it (always the first choice)
└─ No → Is it slow, non-deterministic, or expensive?
         ├─ Yes → Is a FAKE available? (in-memory DB, fake server)
         │        ├─ Yes → Use the fake
         │        └─ No → STUB specific return values (keep count low)
         └─ No → Do I need to verify external SIDE EFFECTS?
                  (email sent, record saved, event published)
                  ├─ Yes → Interaction test with verify (LAST RESORT)
                  └─ No → Re-examine — you probably CAN use the real thing

When to Mock

Mock AT boundaries (external edges of your system):

  • External HTTP APIs → fake server or stub responses
  • Databases → in-memory DB, fake repository, or testcontainers
  • File system → in-memory FS or temp directories
  • System clock → inject controllable clock
  • Non-deterministic sources → seeded random, fixed UUIDs
  • Expensive third-party calls → stub at the adapter layer

Never mock these:

  • Internal collaborator classes
  • Value objects or data structures
  • Pure functions or utilities
  • Anything you can instantiate cheaply

"Don't Mock What You Don't Own": If you must mock a third-party API, wrap it in your own adapter and mock the adapter.

What to Test

Think in behaviors, not methods. Each test covers one behavior: "Given X, when Y, then Z."

Identify Behaviors

Don't write one test per method. Write one test per behavior:

python
# BAD — one method, one test (grows unwieldy)
def test_process_transaction():
    # tests display, validation, AND balance check in one test
    ...

# GOOD — one behavior, one test
def test_process_transaction_displays_item_name(): ...
def test_process_transaction_rejects_negative_amount(): ...
def test_process_transaction_warns_on_low_balance(): ...

Priority Order

  1. Edge cases and error conditions — these catch real bugs
  2. Business rules and invariants — the core logic
  3. Integration boundaries — where systems meet
  4. Happy path — last, not first (it's usually the most obvious)

Usually Skip

Third-party library internals, simple getters/setters, framework boilerplate, and implementation details that may change.

Bug Fixes

Always write a failing test FIRST that reproduces the bug. Then fix it. Never fix a bug without a regression test.

A failure you found by exercising the code by hand is a bug like any other — reproduce the observed failure in a failing test before fixing it, then re-exercise by hand to confirm.

Anti-Patterns: Before and After

1. Over-Mocking → Use Real Implementations

python
# BEFORE — testing mocks, not code
@patch("myapp.cache.get")
@patch("myapp.db.query")
@patch("myapp.validator.check")
def test_process(mock_check, mock_cache, mock_db):
    mock_db.return_value = {"id": 1}
    mock_cache.return_value = None
    mock_check.return_value = True
    result = process(1)  # What are we even testing?
    assert result == {"id": 1}

# AFTER — test with real collaborators
def test_process():
    db = InMemoryDatabase({"users": [{"id": 1, "name": "Alice"}]})
    service = ProcessingService(db=db, cache=MemoryCache())
    result = service.process(1)
    assert result.name == "Alice"

2. Mirror Tests → Test Outcomes, Not Steps

python
# BEFORE — mirrors implementation step by step
def test_register_user():
    service.register("alice@test.com", "pass123")
    mock_validator.validate.assert_called_once_with("alice@test.com")
    mock_db.insert.assert_called_once()
    mock_email.send.assert_called_once_with(
        to="alice@test.com", template="welcome"
    )

# AFTER — asserts observable outcomes
def test_register_user():
    service.register("alice@test.com", "pass123")
    assert service.get_user("alice@test.com") is not None  # user exists
    assert len(email_server.sent) == 1                      # email sent
    assert email_server.sent[0].to == "alice@test.com"

Writing Good Tests

Naming Conventions

Describe what scenario is tested and what outcome is expected:

python
# Good — scenario + expected outcome
def test_expired_token_returns_401(): ...
def test_checkout_with_empty_cart_raises_error(): ...
def test_transfer_insufficient_balance_raises_error(): ...
def test_login_wrong_password_locks_after_3_attempts(): ...
def test_search_returns_empty_list_when_no_matches(): ...

# Bad — vague, no outcome
def test_token_expiry_check(): ...
def test_transfer(): ...
def test_login_error(): ...
def test_search_works(): ...

Test Structure: AAA / GWT

Structure each test as Arrange → Act → Assert (or Given → When → Then). Keep sections visually distinct — whitespace or comments between the three phases help readability.

DAMP, Not DRY

Tests should be Descriptive And Meaningful Phrases. Duplicate for clarity:

  • Use helper methods for constructing test objects (factories with sensible defaults)
  • Use factory helpers like make_user(**overrides) to build test objects with sensible defaults — override only fields relevant to the test
  • Avoid helpers that hide what's being asserted
  • Each test should be readable without scrolling to shared setup
  • Prefer explicit inline values over shared constants with ambiguous names

No Logic in Test Bodies

Tests should be trivially correct on inspection. Straight-line code only:

  • ❌ No loops, conditionals, or string concatenation in assertions
  • ❌ No computed expected values
  • ✅ Hardcode every expected value
  • ✅ One clear path from setup → action → assertion

Test Quality Checklist

Before committing tests, verify each one:

markdown
- [ ] Passes the 5-question Self-Test (above)
- [ ] Name describes scenario + expected outcome
- [ ] No logic (loops, conditionals) in test body
- [ ] Each test is self-contained (DAMP: readable without shared context)
- [ ] Failure message tells you what's wrong without reading the test
- [ ] Uses real implementations where possible (mocks only at boundaries)
- [ ] Tests behavior through public API, not internal methods

Rationalization Prevention

ExcuseRealityRequired Action
"The change is too small to test"Small changes cause regressionsWrite at least one test for the behavior
"Tests are passing"You haven't actually run themRun the test suite and show output
"Existing tests cover this"You haven't checkedFind and cite the specific test
"I'll add tests later"Later never comesWrite tests before marking done
"Mocking is too complex here"Complex mocking means bad designRefactor to test real behavior instead
"This is just a prototype"Prototypes without tests become production codeWrite at least a smoke test for core behavior
"I found it by hand, I'll just fix it"An unfixed-in-tests bug comes backWrite the failing test that reproduces what you observed, then fix

Frequently asked questions

What does the Testing AI skill do?

Behavioral testing strategy — deciding what to test and how. Use when writing tests, reviewing test quality, or fixing tests that test mocks instead of behavior. Triggers on: 'use testing mode', 'write tests', 'test strategy', 'tests are brittle', 'tests test mocks', 'improve test quality', 'what should I test'. Full access mode - can write and run tests.

Why use Testing on TypingMind?

Because you install it once and use it with any model. Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mcouthon/agents/tree/main/generated/claude/skills/testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Testing?

As many as you like. As long as a model supports skills, you can use Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Testing AI skill free?

Yes. It is published on GitHub by mcouthon under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇