tdd-workflows-tdd-green

$npx mdskill add benjaminasterA/antigravity-awesome-skills/tdd-workflows-tdd-green

Implement minimal code to make failing tests pass in TDD green phase.

  • Solves the need to quickly satisfy failing tests with minimal implementation.
  • Depends on the existing test suite and codebase under test.
  • Decides based on test failures to produce the simplest passing code.
  • Delivers code changes directly to the source files for immediate test execution.

SKILL.md

.github/skills/tdd-workflows-tdd-greenView on GitHub ↗
---
name: tdd-workflows-tdd-green
description: "Implement the minimal code needed to make failing tests pass in the TDD green phase."
risk: unknown
source: community
---

# Green Phase: Simple function
def product_list(request):
    products = Product.objects.all()
    return JsonResponse({'products': list(products.values())})

# Refactor: Class-based view
class ProductListView(View):
    def get(self, request):
        products = Product.objects.all()
        return JsonResponse({'products': list(products.values())})

# Refactor: Generic view
class ProductListView(ListView):
    model = Product
    context_object_name = 'products'
```

### Express Patterns

**Inline → Middleware → Service Layer:**
```javascript
// Green Phase: Inline logic
app.post('/api/users', (req, res) => {
  const user = { id: Date.now(), ...req.body };
  users.push(user);
  res.json(user);
});

// Refactor: Extract middleware
app.post('/api/users', validateUser, (req, res) => {
  const user = userService.create(req.body);
  res.json(user);
});

// Refactor: Full layering
app.post('/api/users',
  validateUser,
  asyncHandler(userController.create)
);
```

## Use this skill when

- Moving from red to green in a TDD cycle
- Implementing minimal behavior to satisfy tests
- You want to keep implementation intentionally simple

## Do not use this skill when

- You are refactoring for design or performance
- Tests are already passing and you need new requirements
- You need a full architectural redesign

## Instructions

1. Review failing tests and identify the smallest fix.
2. Implement the minimal change to pass the next test.
3. Run tests after each change to confirm progress.
4. Record shortcuts or debt for the refactor phase.

## Safety

- Avoid bypassing tests to make them pass.
- Keep changes scoped to the failing behavior only.

## Resources

- `resources/implementation-playbook.md` for detailed patterns and examples.

More from benjaminasterA/antigravity-awesome-skills

SkillDescription
2d-games2D game development principles. Sprites, tilemaps, physics, camera.
3d-games3D game development principles. Rendering, shaders, physics, cameras.
3d-web-experienceExpert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ...
accessibility-compliance-accessibility-auditYou are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
active-directory-attacksThis skill should be used when the user asks to \"attack Active Directory\", \"exploit AD\", \"Kerberoasting\", \"DCSync\", \"pass-the-hash\", \"BloodHound enumeration\", \"Golden Ticket\", ...
activecampaign-automationAutomate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
address-github-commentsUse when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
agent-framework-azure-ai-pyBuild Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code int...
agent-manager-skillManage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
agent-memory-mcpA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).