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ai-first-engineering

Design processes, reviews, and architectures for AI-assisted code generation teams.

1|Updated Mar 18, 2026
Search Tags:#architecture#ai#engineering#process#ai-first#code-generation
One-click install
npx skills add https://github.com/ValenBorras/upscribe --skill ai-first-engineering-valenborras
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-first-engineering
Source: https://github.com/ValenBorras/upscribe/tree/main/.github/skills/ai-first-engineering
Command: npx skills add https://github.com/ValenBorras/upscribe --skill ai-first-engineering-valenborras

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design processes, reviews, and architectures for teams shipping with AI-assisted code generation.

Core Features & Use Cases

  • Process Shifts: Emphasize planning quality, eval coverage, and system behavior focus.
  • Architecture Requirements: Prefer agent-friendly designs with explicit boundaries, stable contracts, typed interfaces, and deterministic tests.
  • Code Review in AI-First Teams: Focus on behavior, security, data integrity, failure handling, and rollout safety; minimize time on style issues automated.
  • Hiring and Evaluation Signals: Engineers who decompose work, define acceptance criteria, produce prompts/evals, and enforce risk controls.
  • Testing Standard: Require regression coverage, explicit edge-case assertions, and integration checks for interfaces.

Quick Start

Describe your AI-enabled project context so this Skill can tailor an AI-first engineering plan and governance approach.

Frequently Asked Questions about ai-first-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is AI-first engineering and how does it change software development processes?▼

AI-first engineering is a development approach that designs processes, reviews, and architectures specifically for teams shipping with AI-assisted code generation, emphasizing planning quality, eval coverage, and system behavior focus over traditional manual implementation.

How do I design architecture for AI-assisted code generation?▼

To design architecture for AI-assisted code generation, prefer agent-friendly designs with explicit boundaries, stable contracts, typed interfaces, and deterministic tests to ensure reliable AI-driven delivery and minimize integration failures.

What should code review focus on in teams using AI code generation?▼

Code review in AI-first teams should focus on behavior, security, data integrity, failure handling, and rollout safety, minimizing time spent on style issues that can be automated by the AI tooling.

What are the testing standards for AI-first development?▼

Testing standards for AI-first development require regression coverage, explicit edge-case assertions, and integration checks for interfaces to enforce strong guardrails and deterministic behavior for AI-generated code.

What engineering signals should I evaluate when hiring for AI-first teams?▼

When hiring for AI-first teams, evaluate engineers who effectively decompose work, define acceptance criteria, produce prompts and evals, and enforce risk controls to ensure robust AI-driven delivery.

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