Why Your AI Agent Needs Product Skills (Not Just Code Skills)

The Problem With “AI Coding Assistants”

Every AI coding tool promises the same thing: “Write code faster.”

And they deliver. Sort of.

You ask for a login form, you get a login form. You ask for an API endpoint, you get an API endpoint. The code compiles. The tests pass.

But is it production-ready?

• Does it have proper tracking pixels?
• Does it follow your design system?
• Are there health checks?
• What about error boundaries?
• Feature flags for safe rollout?
• Accessibility attributes?

Usually not.

Because AI coding assistants are trained to write code, not to ship products.

From Code Assistant to Product Engineering Assistant

Here’s the insight that changes everything:

Great engineering isn’t about writing code. It’s about shipping products that work.

A senior engineer doesn’t just implement features. They:

1. Follow design system patterns (not reinvent UI components)
2. Add tracking from day one (not retrofit analytics later)
3. Build in observability (not debug in production)
4. Consider accessibility (not fail audits)
5. Use feature flags (not deploy and pray)

What if your AI assistant thought the same way?

Introducing Product Skills

GPTCode’s Skills are prompt injections that encode best practices beyond syntax. They’re not just “write Go code” — they’re “write Go code that handles errors properly, uses context for cancellation, and follows interface segregation.”

And now, we have Product Skills:

Design System Skill

When you’re building UI, the AI doesn’t just create a button:

// Without skill: Whatever works
<button style=>Submit</button>

// With design-system skill: Atomic design + tokens
<Button variant="primary" size="md">Submit</Button>

The skill teaches:

• Atomic Design (atoms → molecules → organisms)
• Design tokens (semantic color names, spacing scale)
• Component API patterns (composition over configuration)
• Storybook documentation
• ARIA accessibility

Product Metrics Skill

Every feature needs tracking. The AI now knows:

// Without skill: No tracking
handleSubmit(data);

// With product-metrics skill: Full attribution
analytics.track('form_submitted', {
  form_name: 'checkout',
  ...getUTMContext(),
  ...getUserContext(),
});
handleSubmit(data);

The skill covers:

• Event naming conventions (object_action pattern)
• UTM parameter handling
• Conversion funnel tracking
• Core Web Vitals monitoring
• Privacy-compliant analytics

Production Ready Skill

Features ship with guardrails:

// Without skill: Crashes on error
<UserProfile userId={id} />

// With production-ready skill: Graceful degradation
<ErrorBoundary fallback={<ProfileSkeleton />}>
  <Suspense fallback={<ProfileLoading />}>
    <UserProfile userId={id} />
  </Suspense>
</ErrorBoundary>

The skill includes:

• Error boundaries (React, Vue patterns)
• Feature flags (gradual rollouts)
• Health checks (readiness/liveness probes)
• Circuit breakers (graceful degradation)
• Structured logging (observability)

QA Automation Skill

Tests aren’t an afterthought:

// Without skill: Fragile selectors
await page.click('.btn.btn-primary');

// With qa-automation skill: Stable, accessible
await page.click('[data-testid="submit-order"]');
await expect(page).toHaveScreenshot('checkout-complete.png');
await new AxeBuilder({ page }).analyze();

The skill teaches:

• Page Object Model (maintainable E2E)
• Visual regression (catch UI breaks)
• Accessibility testing (axe-core integration)
• Performance budgets (Lighthouse CI)

The Compound Effect

Here’s where it gets interesting.

Each skill alone improves code quality. Together, they compound.

When you ask gt do "add checkout flow", the AI:

1. Analyzes your codebase (finds existing components)
2. Plans with all skills loaded (design system + metrics + production + QA)
3. Generates code that:
   • Uses your existing Button, Input, Card components
   • Includes form_submitted, purchase_completed events
   • Wraps in ErrorBoundary with fallback
   • Has data-testid on interactive elements
4. Validates that tests pass

One command. Production-ready feature.

Why This Matters

Junior → Senior Encoding

Every skill encodes patterns that take years to learn:

Your AI assistant now has 10+ years of “senior engineering intuition.”

Team Consistency

When everyone’s AI uses the same skills:

• Components look the same
• Events follow the same naming
• Error handling is consistent
• Tests use the same patterns

No more “I didn’t know we have a Button component.”

Quality By Default

Instead of catching issues in code review:

• Design system violations → prevented
• Missing tracking → impossible
• No error handling → doesn’t ship
• Inaccessible UI → flagged

Shift left, all the way to generation.

How To Use Skills

Built-in with GPTCode

# Skills are automatically injected based on project language
gt do "add user profile page"
# → Uses: TypeScript skill + Design System skill + Product Metrics skill

Export to Other Tools

# Export for Cursor
gt skills show design-system > .cursorrules

# Export for VS Code Copilot
gt skills show production-ready > .github/copilot-instructions.md

# Export for Claude/Gemini
gt skills show product-metrics  # Copy to system prompt

Create Custom Skills

# Your company's conventions
vi ~/.gptcode/skills/our-company.md

---
name: our-company-style
language: typescript
description: Our internal conventions
---

# Company Conventions

## All API calls must...
## All components must...

The 15 Skills Available Today

Language-Specific

• Go, Ruby, Rails, Python, TypeScript, JavaScript, Elixir, Rust

Workflow

• TDD Bug Fix, Code Review, Git Commit

Product Engineering (NEW)

• Design System — Atomic design, tokens, Storybook
• Product Metrics — GA4, UTM, funnels, pixels
• Production Ready — Error handling, feature flags, health checks
• QA Automation — E2E, visual regression, accessibility

The Shift

AI coding assistants write code.

Product Engineering Assistants ship products.

The difference isn’t the underlying model. It’s the skills — the accumulated wisdom of senior engineers, encoded and injected into every prompt.

When your AI knows what “production-ready” means, it delivers production-ready code.

Try it now:

# Install
curl -sSL https://gptcode.dev/install.sh | bash

# Run with skills
gt do "add user authentication with proper tracking and error handling"

# See what skills were used
gt skills list

Explore all skills: gptcode.cloud/skills

What product engineering patterns should we add next? Open an issue with your suggestions.