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Tdd Mastery

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rohitg00
tdd-mastery

Test-driven development workflow with Red-Green-Refactor cycle across languages

Overview

Publisherrohitg00
Repositoryawesome-claude-code-toolkit
Skill nametdd-mastery
Stars
2.6K
Forks
963
Bundled files
Instructions only
LicenseApache-2.0
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Tdd Mastery 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/rohitg00/awesome-claude-code-toolkit.git /tmp/awesome-claude-code-toolkit
mkdir -p .claude/skills
cp -r /tmp/awesome-claude-code-toolkit/skills/tdd-mastery .claude/skills/tdd-mastery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tdd Mastery 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 Tdd Mastery 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 Tdd Mastery 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.

TDD Mastery

Core Cycle: Red-Green-Refactor

  1. Red - Write a failing test that defines the desired behavior
  2. Green - Write the minimum code to make the test pass
  3. Refactor - Clean up while keeping tests green

Never write production code without a failing test first. Each cycle should take 2-10 minutes.

Test Structure

Use the Arrange-Act-Assert pattern consistently:

Arrange: Set up test data and dependencies
Act:     Execute the behavior under test
Assert:  Verify the expected outcome

Name tests as test_<unit>_<scenario>_<expected_result> or it("should <behavior> when <condition>").

Jest / Vitest Patterns

typescript
describe("OrderService", () => {
  it("should apply discount when order exceeds threshold", () => {
    const order = createOrder({ items: [{ price: 150, qty: 1 }] });
    const result = applyDiscount(order, { threshold: 100, percent: 10 });
    expect(result.total).toBe(135);
  });

  it("should throw when applying discount to empty order", () => {
    const order = createOrder({ items: [] });
    expect(() => applyDiscount(order, defaultDiscount)).toThrow(EmptyOrderError);
  });
});

Use vi.fn() / jest.fn() for mocks. Prefer dependency injection over module mocking. Use beforeEach for shared setup, never share mutable state between tests.

pytest Patterns

python
@pytest.fixture
def db_session():
    session = create_test_session()
    yield session
    session.rollback()

def test_create_user_stores_hashed_password(db_session):
    user = UserService(db_session).create(email="a@b.com", password="secret")
    assert user.password_hash != "secret"
    assert verify_password("secret", user.password_hash)

@pytest.mark.parametrize("input,expected", [
    ("", False),
    ("short", False),
    ("ValidPass1!", True),
])
def test_password_validation(input, expected):
    assert validate_password(input) == expected

Use pytest.raises for exceptions. Use conftest.py for shared fixtures. Mark slow tests with @pytest.mark.slow.

Go Testing Patterns

go
func TestParseConfig(t *testing.T) {
    tests := []struct {
        name    string
        input   string
        want    Config
        wantErr bool
    }{
        {"valid yaml", "port: 8080", Config{Port: 8080}, false},
        {"empty input", "", Config{}, true},
        {"invalid port", "port: -1", Config{}, true},
    }
    for _, tt := range tests {
        t.Run(tt.name, func(t *testing.T) {
            got, err := ParseConfig([]byte(tt.input))
            if (err != nil) != tt.wantErr {
                t.Errorf("ParseConfig() error = %v, wantErr %v", err, tt.wantErr)
                return
            }
            if !tt.wantErr && got != tt.want {
                t.Errorf("ParseConfig() = %v, want %v", got, tt.want)
            }
        })
    }
}

Use table-driven tests by default. Use t.Helper() in test utility functions. Use testify/assert only if the team already uses it.

Test Levels

LevelScopeSpeedDependencies
UnitSingle function/class<100msNone (mock all)
IntegrationModule boundaries<5sReal DB, real FS
E2EFull user flow<30sFull stack

Ratio target: 70% unit, 20% integration, 10% e2e.

Coverage Rules

  • Enforce 80% line coverage minimum in CI
  • Track branch coverage, not just line coverage
  • Exclude generated code, type definitions, and config files
  • Never write tests just to hit coverage numbers; test behavior
bash
# Jest/Vitest
vitest run --coverage --coverage.thresholds.lines=80 --coverage.thresholds.branches=75

# pytest
pytest --cov=src --cov-fail-under=80 --cov-branch

# Go
go test -coverprofile=cover.out -coverpkg=./... ./...
go tool cover -func=cover.out

Mocking Guidelines

  • Mock at boundaries: HTTP clients, databases, file systems, clocks
  • Never mock the unit under test
  • Prefer fakes (in-memory implementations) over mocks for repositories
  • Assert on behavior, not on mock call counts
  • Use t.Cleanup / afterEach to reset shared mocks

Anti-Patterns to Avoid

  • Testing implementation details instead of behavior
  • Tests that pass when code is deleted (tautological tests)
  • Shared mutable state between test cases
  • Ignoring flaky tests instead of fixing them
  • Testing private methods directly
  • Giant test setup that obscures intent

Frequently asked questions

What does the Tdd Mastery AI skill do?

Test-driven development workflow with Red-Green-Refactor cycle across languages

Why use Tdd Mastery on TypingMind?

Because you install it once and use it with any model. Tdd Mastery 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 Tdd Mastery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/awesome-claude-code-toolkit/tree/main/skills/tdd-mastery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tdd Mastery?

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 Tdd Mastery?

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

Is the Tdd Mastery AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 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.

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