test-generator
Generate comprehensive test cases for code. Use when writing unit tests, integration tests, or test suites. Creates tests that cover happy paths, edge cases, error conditions, and follow testing best practices for the project's testing framework.
What this skill does
# Test Generator Skill
## Instructions
1. Analyze the code to understand what needs testing
2. Identify test cases:
- Happy path scenarios
- Edge cases (empty inputs, null values, boundary conditions)
- Error conditions and exception handling
- Integration points and dependencies
3. Generate tests following project conventions:
- Use appropriate testing framework (Jest, pytest, Mocha, etc.)
- Follow naming conventions (describe/it, test_*, etc.)
- Use clear, descriptive test names
- Include setup/teardown when needed
- Mock external dependencies appropriately
4. Ensure tests are:
- Independent and isolated
- Fast and deterministic
- Well-organized with good structure
- Include assertions that verify expected behavior
5. Add comments explaining complex test scenarios
## Examples
**Input (Python function):**
```python
def divide(a, b):
if b == 0:
raise ValueError("Cannot divide by zero")
return a / b
```
**Output:**
```python
import pytest
from calculator import divide
class TestDivide:
"""Test suite for divide function."""
def test_divide_positive_numbers(self):
"""Test dividing two positive numbers."""
assert divide(10, 2) == 5.0
assert divide(9, 3) == 3.0
def test_divide_negative_numbers(self):
"""Test dividing negative numbers."""
assert divide(-10, 2) == -5.0
assert divide(10, -2) == -5.0
def test_divide_by_zero_raises_error(self):
"""Test that dividing by zero raises ValueError."""
with pytest.raises(ValueError, match="Cannot divide by zero"):
divide(10, 0)
def test_divide_float_result(self):
"""Test that division returns float when result is decimal."""
assert divide(7, 2) == 3.5
def test_divide_zero_numerator(self):
"""Test dividing zero by non-zero number."""
assert divide(0, 5) == 0.0
```
**Input (JavaScript function):**
```javascript
function validateEmail(email) {
if (!email) return false;
return /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email);
}
```
**Output:**
```javascript
describe('validateEmail', () => {
test('returns true for valid email addresses', () => {
expect(validateEmail('[email protected]')).toBe(true);
expect(validateEmail('[email protected]')).toBe(true);
});
test('returns false for invalid email addresses', () => {
expect(validateEmail('invalid')).toBe(false);
expect(validateEmail('@example.com')).toBe(false);
expect(validateEmail('user@')).toBe(false);
expect(validateEmail('user@example')).toBe(false);
});
test('returns false for empty or null input', () => {
expect(validateEmail('')).toBe(false);
expect(validateEmail(null)).toBe(false);
expect(validateEmail(undefined)).toBe(false);
});
test('handles edge cases', () => {
expect(validateEmail('[email protected]')).toBe(true);
expect(validateEmail('[email protected]')).toBe(true);
});
});
```
## Testing Best Practices
**Structure:**
- Group related tests in describe/test classes
- Use descriptive test names that explain what is being tested
- Follow AAA pattern: Arrange, Act, Assert
**Coverage:**
- Test happy paths
- Test edge cases and boundary conditions
- Test error conditions
- Test with different input types
- Test with null/undefined/empty values
**Framework-Specific:**
- **Jest/Mocha**: Use `describe`/`it` or `test`
- **pytest**: Use `test_` prefix, `pytest.fixture` for setup
- **JUnit**: Use `@Test` annotation, `@BeforeEach` for setup
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.