ai-first-engineering

Sets up engineering practices for teams using AI agents to write code at scale.

Installation
Run `npx skills add "https://github.com/affaan-m/ecc" --skill "ai-first-engineering"` to install this skill, then follow its SKILL.md instructions for my next request.

Paste this into Claude Code, Cursor, or any agent that can run commands.

What this skill does
What it does: - Teaches teams how to work when AI tools write most of the code - Changes how teams plan, check code, and build software for AI-generated code - Focuses reviews on whether code works correctly instead of style issues - Requires clear rules and strong tests for AI-generated code When to use it: - When your team uses AI coding assistants like Claude Code to write code - When you need to set up processes that work with lots of AI-generated output - When you want to make sure AI-generated code is safe and reliable - When designing how your team should review and test code from AI
SKILL.mdShow the author's original SKILL.md
---
name: ai-first-engineering
description: Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.
metadata:
  origin: ECC
---

# AI-First Engineering

Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.

## Process Shifts

1. Planning quality matters more than typing speed.
2. Eval coverage matters more than anecdotal confidence.
3. Review focus shifts from syntax to system behavior.

## Architecture Requirements

Prefer architectures that are agent-friendly:
- explicit boundaries
- stable contracts
- typed interfaces
- deterministic tests

Avoid implicit behavior spread across hidden conventions.

## Code Review in AI-First Teams

Review for:
- behavior regressions
- security assumptions
- data integrity
- failure handling
- rollout safety

Minimize time spent on style issues already covered by automation.

## Hiring and Evaluation Signals

Strong AI-first engineers:
- decompose ambiguous work cleanly
- define measurable acceptance criteria
- produce high-signal prompts and evals
- enforce risk controls under delivery pressure

## Testing Standard

Raise testing bar for generated code:
- required regression coverage for touched domains
- explicit edge-case assertions
- integration checks for interface boundaries

Mirrored from the author's public source. Install counts from the open skills registry.

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