red-green-refactor
Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.
Best use case
red-green-refactor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.
Teams using red-green-refactor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/red-green-refactor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How red-green-refactor Compares
| Feature / Agent | red-green-refactor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Red-Green-Refactor Methodology
You are following the RED-GREEN-REFACTOR cycle for test-driven development. Every new feature, bug fix, or behavior change starts with a failing test.
## The Cycle
### 1. RED Phase — Write a Failing Test
1. **Understand the requirement** — what specific behavior must exist?
2. **Write one test** asserting that behavior
3. **Run the test** — it MUST fail (red)
4. **Verify the failure reason** — not a syntax error, but a missing implementation
The test should be focused on ONE behavior, named descriptively, and use clear assertions.
**Executable example (Jest):**
```js
// calculateTotal.test.js
const { calculateTotal } = require('./calculateTotal');
describe('calculateTotal', () => {
it('should apply 10% discount when total exceeds 100', () => {
const items = [{ price: 60 }, { price: 60 }]; // total = 120
expect(calculateTotal(items)).toBe(108); // 120 * 0.90
});
});
```
Running this now produces: `Cannot find module './calculateTotal'` — correct RED state.
---
### 2. GREEN Phase — Make the Test Pass
Write the **minimum code** needed to pass the test. Don't add anything extra.
```js
// calculateTotal.js
function calculateTotal(items) {
const total = items.reduce((sum, item) => sum + item.price, 0);
return total > 100 ? total * 0.9 : total;
}
module.exports = { calculateTotal };
```
Run the test — it passes. GREEN achieved. Stop here; resist adding more logic.
---
### 3. REFACTOR Phase — Improve the Code
With a passing test as your safety net, clean up the implementation. Run tests after every change.
```js
// calculateTotal.js — refactored for clarity
const DISCOUNT_THRESHOLD = 100;
const DISCOUNT_RATE = 0.9;
function calculateTotal(items) {
const subtotal = items.reduce((sum, { price }) => sum + price, 0);
return subtotal > DISCOUNT_THRESHOLD ? subtotal * DISCOUNT_RATE : subtotal;
}
module.exports = { calculateTotal };
```
Test still passes — GREEN maintained. Constants now communicate intent.
---
## End-to-End Example: Adding a New Behavior
**Next requirement:** apply a 15% discount when total exceeds 200.
**RED** — write the failing test first:
```js
it('should apply 15% discount when total exceeds 200', () => {
const items = [{ price: 110 }, { price: 110 }]; // total = 220
expect(calculateTotal(items)).toBe(187); // 220 * 0.85
});
```
**GREEN** — extend the implementation minimally:
```js
function calculateTotal(items) {
const subtotal = items.reduce((sum, { price }) => sum + price, 0);
if (subtotal > 200) return subtotal * 0.85;
if (subtotal > 100) return subtotal * 0.9;
return subtotal;
}
```
**REFACTOR** — remove duplication with a tiered structure:
```js
const DISCOUNT_TIERS = [
{ threshold: 200, rate: 0.85 },
{ threshold: 100, rate: 0.9 },
];
function calculateTotal(items) {
const subtotal = items.reduce((sum, { price }) => sum + price, 0);
const tier = DISCOUNT_TIERS.find(({ threshold }) => subtotal > threshold);
return tier ? subtotal * tier.rate : subtotal;
}
```
Both tests pass — ready for the next cycle.
---
## Workflow Steps
1. **Create or open the test file first**
2. **Write ONE failing test** for the smallest testable unit
3. **Implement minimally** — just enough to pass
4. **Refactor if needed** — while tests stay green
5. **Repeat** for the next behavior
## Decision Points
### Write a new test when:
- Adding a new feature or behavior
- Fixing a bug (test the bug first, then fix it)
- Handling an edge case discovered during implementation
### Don't write a test when:
- Pure refactoring (existing tests already cover the behavior)
- Non-functional changes (formatting, comments)
- Third-party library internals
## Verification Checklist
- [ ] All new code has corresponding tests
- [ ] Tests fail when the feature is removed
- [ ] Tests pass consistently (not flaky)
- [ ] Code has been refactored for clarity
- [ ] No unnecessary code was added
## Common Mistakes to Avoid
1. **Writing tests after code** — defeats the design benefit of TDD
2. **Writing multiple tests at once** — one test drives one change
3. **Passing tests with hacks** — the test should drive good design
4. **Skipping the refactor phase** — technical debt accumulates
5. **Testing implementation details** — test behavior, not internals
## Integration with Other Skills
- **test-patterns**: Patterns for structuring tests
- **anti-patterns**: Common testing mistakes to avoid
- **debugging/root-cause-analysis**: When tests reveal unexpected failuresRelated Skills
find-skills
Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.
test-patterns
Applies proven testing patterns — Arrange-Act-Assert (AAA), Given-When-Then, Test Data Builders, Object Mother, parameterized tests, fixtures, spies, and test doubles — to help write maintainable, reliable, and readable test suites. Use when the user asks about writing unit tests, integration tests, or end-to-end tests; structuring test cases or test suites; applying TDD or BDD practices; working with mocks, stubs, spies, or fakes; improving test coverage or reducing flakiness; or needs guidance on test organization, naming conventions, or assertions in frameworks like Jest, Vitest, pytest, or similar.
testing-anti-patterns
Reviews test code to identify and fix common testing anti-patterns including flaky tests, over-mocking, brittle assertions, test interdependency, and hidden test logic. Flags bad patterns, explains the specific defect, and provides corrected implementations. Use when reviewing test code, debugging intermittent or unreliable test failures, or when the user mentions flaky tests, test smells, brittle tests, test isolation issues, mock overuse, slow tests, or test maintenance problems.
verification-gates
Creates explicit validation checkpoints (verification gates) between project phases to catch errors early and ensure quality before proceeding. Use when the user asks about quality gates, milestone checks, phase transitions, approval steps, go/no-go decision points, or preventing cascading errors across a multi-step workflow. Produces acceptance criteria checklists, automated CI gate configurations, manual sign-off requirements, and conditional review rules for scenarios such as security changes, API changes, or database migrations.
task-decomposition
Breaks down complex software, writing, or research tasks into small, atomic, independently completable units with dependency graphs and milestone breakdowns. Use when the user asks to plan a project, decompose a feature, create subtasks, split up work, or needs help organizing a large piece of work into a step-by-step plan. Triggered by phrases like "break down", "decompose", "where do I start", "too big", "split into tasks", "work breakdown", or "task list".
design-first
Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.
skill-authoring
Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.
trace-and-isolate
Applies systematic tracing and isolation techniques to pinpoint exactly where a bug originates in code. Use when a bug is hard to locate, code is not working as expected, an error or crash appears with unclear cause, a regression was introduced between recent commits, or you need to narrow down which component, function, or line is faulty. Covers binary search debugging, git bisect for regressions, strategic logging with [TRACE] patterns, data and control flow tracing, component isolation, minimal reproduction cases, conditional breakpoints, and watch expressions across TypeScript, SQL, and bash.
root-cause-analysis
Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.
hypothesis-testing
Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
structured-code-review
Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".
parallel-investigation
Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.