tdd-guide
Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and Mocha. Use when the user asks to write tests, improve test coverage, practice TDD, generate mocks or stubs, or mentions testing frameworks like Jest, pytest, or JUnit.
What this skill does
# TDD Guide
Test-driven development skill for generating tests, analyzing coverage, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, and Vitest.
---
## Workflows
### Generate Tests from Code
1. Provide source code (TypeScript, JavaScript, Python, Java)
2. Specify target framework (Jest, Pytest, JUnit, Vitest)
3. Run `test_generator.py` with requirements
4. Review generated test stubs
5. **Validation:** Tests compile and cover happy path, error cases, edge cases
### Analyze Coverage Gaps
1. Generate coverage report from test runner (`npm test -- --coverage`)
2. Run `coverage_analyzer.py` on LCOV/JSON/XML report
3. Review prioritized gaps (P0/P1/P2)
4. Generate missing tests for uncovered paths
5. **Validation:** Coverage meets target threshold (typically 80%+)
### TDD New Feature
1. Write failing test first (RED)
2. Run `tdd_workflow.py --phase red` to validate
3. Implement minimal code to pass (GREEN)
4. Run `tdd_workflow.py --phase green` to validate
5. Refactor while keeping tests green (REFACTOR)
6. **Validation:** All tests pass after each cycle
---
## Examples
### Test Generation — Input → Output (Pytest)
**Input source function (`math_utils.py`):**
```python
def divide(a: float, b: float) -> float:
if b == 0:
raise ValueError("Cannot divide by zero")
return a / b
```
**Command:**
```bash
python scripts/test_generator.py --input math_utils.py --framework pytest
```
**Generated test output (`test_math_utils.py`):**
```python
import pytest
from math_utils import divide
class TestDivide:
def test_divide_positive_numbers(self):
assert divide(10, 2) == 5.0
def test_divide_negative_numerator(self):
assert divide(-10, 2) == -5.0
def test_divide_float_result(self):
assert divide(1, 3) == pytest.approx(0.333, rel=1e-3)
def test_divide_by_zero_raises_value_error(self):
with pytest.raises(ValueError, match="Cannot divide by zero"):
divide(10, 0)
def test_divide_zero_numerator(self):
assert divide(0, 5) == 0.0
```
---
### Coverage Analysis — Sample P0/P1/P2 Output
**Command:**
```bash
python scripts/coverage_analyzer.py --report lcov.info --threshold 80
```
**Sample output:**
```
Coverage Report — Overall: 63% (threshold: 80%)
P0 — Critical gaps (uncovered error paths):
auth/login.py:42-58 handle_expired_token() 0% covered
payments/process.py:91-110 handle_payment_failure() 0% covered
P1 — High-value gaps (core logic branches):
users/service.py:77 update_profile() — else branch 0% covered
orders/cart.py:134 apply_discount() — zero-qty guard 0% covered
P2 — Low-risk gaps (utility / helper functions):
utils/formatting.py:12 format_currency() 0% covered
Recommended: Generate tests for P0 items first to reach 80% threshold.
```
---
## Key Tools
| Tool | Purpose | Usage |
|------|---------|-------|
| `test_generator.py` | Generate test cases from code/requirements | `python scripts/test_generator.py --input source.py --framework pytest` |
| `coverage_analyzer.py` | Parse and analyze coverage reports | `python scripts/coverage_analyzer.py --report lcov.info --threshold 80` |
| `tdd_workflow.py` | Guide red-green-refactor cycles | `python scripts/tdd_workflow.py --phase red --test test_auth.py` |
| `fixture_generator.py` | Generate test data and mocks | `python scripts/fixture_generator.py --entity User --count 5` |
Additional scripts: `framework_adapter.py` (convert between frameworks), `metrics_calculator.py` (quality metrics), `format_detector.py` (detect language/framework), `output_formatter.py` (CLI/desktop/CI output).
---
## Input Requirements
**For Test Generation:**
- Source code (file path or pasted content)
- Target framework (Jest, Pytest, JUnit, Vitest)
- Coverage scope (unit, integration, edge cases)
**For Coverage Analysis:**
- Coverage report file (LCOV, JSON, or XML format)
- Optional: Source code for context
- Optional: Target threshold percentage
**For TDD Workflow:**
- Feature requirements or user story
- Current phase (RED, GREEN, REFACTOR)
- Test code and implementation status
---
## Spec-First Workflow
TDD is most effective when driven by a written spec. The flow:
1. **Write or receive a spec** — stored in `specs/<feature>.md`
2. **Extract acceptance criteria** — each criterion becomes one or more test cases
3. **Write failing tests (RED)** — one test per acceptance criterion
4. **Implement minimal code (GREEN)** — satisfy each test in order
5. **Refactor** — clean up while all tests stay green
### Spec Directory Convention
```
project/
├── specs/
│ ├── user-auth.md # Feature spec with acceptance criteria
│ ├── payment-processing.md
│ └── notification-system.md
├── tests/
│ ├── test_user_auth.py # Tests derived from specs/user-auth.md
│ ├── test_payments.py
│ └── test_notifications.py
└── src/
```
### Extracting Tests from Specs
Each acceptance criterion in a spec maps to at least one test:
| Spec Criterion | Test Case |
|---------------|-----------|
| "User can log in with valid credentials" | `test_login_valid_credentials_returns_token` |
| "Invalid password returns 401" | `test_login_invalid_password_returns_401` |
| "Account locks after 5 failed attempts" | `test_login_locks_after_five_failures` |
**Tip:** Number your acceptance criteria in the spec. Reference the number in the test docstring for traceability (`# AC-3: Account locks after 5 failed attempts`).
> **Cross-reference:** See `engineering/spec-driven-workflow` for the full spec methodology, including spec templates and review checklists.
---
## Red-Green-Refactor Examples Per Language
### TypeScript / Jest
```typescript
// test/cart.test.ts
describe("Cart", () => {
describe("addItem", () => {
it("should add a new item to an empty cart", () => {
const cart = new Cart();
cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 1 });
expect(cart.items).toHaveLength(1);
expect(cart.items[0].id).toBe("sku-1");
});
it("should increment quantity when adding an existing item", () => {
const cart = new Cart();
cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 1 });
cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 2 });
expect(cart.items).toHaveLength(1);
expect(cart.items[0].qty).toBe(3);
});
it("should throw when quantity is zero or negative", () => {
const cart = new Cart();
expect(() =>
cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 0 })
).toThrow("Quantity must be positive");
});
});
});
```
### Python / Pytest (Advanced Patterns)
```python
# tests/conftest.py — shared fixtures
import pytest
from app.db import create_engine, Session
@pytest.fixture(scope="session")
def db_engine():
engine = create_engine("sqlite:///:memory:")
yield engine
engine.dispose()
@pytest.fixture
def db_session(db_engine):
session = Session(bind=db_engine)
yield session
session.rollback()
session.close()
# tests/test_pricing.py — parametrize for multiple cases
import pytest
from app.pricing import calculate_discount
@pytest.mark.parametrize("subtotal, expected_discount", [
(50.0, 0.0), # Below threshold — no discount
(100.0, 5.0), # 5% tier
(250.0, 25.0), # 10% tier
(500.0, 75.0), # 15% tier
])
def test_calculate_discount(subtotal, expected_discount):
assert calculate_discount(subtotal) == pytest.approx(expected_discount)
```
### Go — Table-Driven Tests
```go
// cart_test.go
package cart
import "testing"
func TestApplyDiscount(t *testing.T) {
tests := []struct {
name string
subtotal float64
want float64
}{
{"no discount below threshold", 50.0, 0.0},
{"5 percent tier", 100.0, 5.0},
{"10 percent tier", 250.0, 25.0},
{"15 percent tier", 500.0, 75.0},
{"zero subtotal", 0.0, 0.0},
Related in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.