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ai-first-engineering

Design engineering processes and architecture for teams shipping AI-generated implementation output.

Updated May 4, 2026
Search Tags:#code review#ai agents#evaluation#testing strategy#system architecture#engineering process#rollout safety
One-click install
npx skills add https://github.com/gganbukim1/myskills --skill ai-first-engineering-gganbukim1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-first-engineering
Source: https://github.com/gganbukim1/myskills/tree/main/ai-first-engineering
Command: npx skills add https://github.com/gganbukim1/myskills --skill ai-first-engineering-gganbukim1

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps teams design repeatable engineering process, review practices, and architecture for environments where AI agents produce a large share of implementation output, reducing the risk of regressions, security gaps, and unreliable behavior.

Core Features & Use Cases

  • Process Shifts for AI Teams: Prioritizes planning quality, eval coverage, and behavior-focused review over typing speed and anecdotal confidence.
  • Agent-Friendly Architecture Requirements: Encourages explicit boundaries, stable contracts, typed interfaces, and deterministic tests to avoid hidden conventions.
  • AI-Aware Code Review & Testing Standards: Focuses reviews on behavior regressions, security assumptions, data integrity, failure handling, and rollout safety while raising regression coverage for touched domains.

Quick Start

Ask your AI agent to propose an AI-first delivery plan that includes measurable acceptance criteria, an eval strategy, and a behavior-focused review checklist for your target service.

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 process design for production software?▼

AI-first engineering process design structures planning, eval coverage, code review, and testing workflows specifically for teams shipping AI-generated implementation output, reducing the risk of behavior regressions and security gaps in production.

How do I review code generated by AI agents for behavior regressions?▼

To review code generated by AI agents, focus your code review on behavior regressions, security assumptions, data integrity, failure handling, and rollout safety while raising deterministic regression test coverage for the touched domains.

How to design system architecture for AI-generated code delivery?▼

Design system architecture for AI-generated code by establishing agent-friendly boundaries, stable contracts, typed interfaces, and deterministic tests to avoid hidden conventions and ensure reliable behavior.

What should an AI-first delivery plan include for rollout safety?▼

An AI-first delivery plan should include measurable acceptance criteria, an explicit eval strategy, and a behavior-focused review checklist to ensure deterministic regression tests with explicit edge-case assertions for safe rollout.

Does AI-generated code require deterministic regression tests with edge-case assertions?▼

Yes, AI-generated code requires deterministic regression tests with explicit edge-case assertions to verify behavior, maintain data integrity, and prevent hidden convention failures during production rollout.

When do I need an AI-aware testing strategy for software development?▼

You need an AI-aware testing strategy when AI agents produce a large share of your implementation output, requiring prioritized eval coverage and behavior regression focus over typing speed and anecdotal confidence.

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