ai-first-engineering

Defines engineering processes, review standards, and testing requirements for teams shipping AI-generated code.

Updated Mar 25, 2026
One-click install
npx skills add https://github.com/Femad-6/my-skills --skill ai-first-engineering-femad-6
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-first-engineering
Source: https://github.com/Femad-6/my-skills/tree/main/.github/skills/ai-first-engineering
Command: npx skills add https://github.com/Femad-6/my-skills --skill ai-first-engineering-femad-6

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams where AI agents generate a large share of code often keep legacy processes designed for human-only authorship, leading to weak planning, shallow reviews, and insufficient test coverage. This Skill provides an operating model for process, architecture, review, and hiring adapted to AI-assisted development. ## Core Features & Use Cases - Process Guidance: Shifts team focus from typing speed to planning quality, eval coverage, and system-level behavior review. - Architecture Requirements: Recommends agent-friendly architectures with explicit boundaries, stable contracts, typed interfaces, and deterministic tests. - Review and Testing Standards: Redirects code review toward behavior regressions, security assumptions, data integrity, and rollout safety, while raising the testing bar for generated code. - Use Case: An engineering manager rolling out AI coding assistants uses this Skill to redefine the team's code review checklist and require regression coverage for every domain touched by generated code. ## Quick Start Ask the AI to apply the ai-first-engineering skill to redesign your team's code review checklist and testing standards for AI-generated code.

Frequently Asked Questions about ai-first-engineering

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

FAQPage Schema
When does the AI-first engineering model not apply?

The model targets teams where AI agents generate a large share of implementation output. Teams with minimal AI-assisted coding gain less from its planning, eval, and review shifts, though the architecture guidance remains broadly useful.