ai-first-engineering
Helps teams use AI to create code. It defines how AI agents work alongside humans, ensuring the generated code is high quality.
What it does
This guide helps teams organize their work when artificial intelligence (AI) writes most of the code. It focuses on how to plan, review, and test AI-generated code to ensure everything works correctly.
When to use it
- ✓When defining how your team will work with AI-generated code.
- ✓When creating rules for reviewing code written by AI.
- ✓When deciding who is responsible for which part of AI-made code.
Example of what to ask your AI
“How can we organize our team so that AI helps us write code efficiently and safely?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It suggests that detailed planning and testing are more important than writing speed. It also asks for software architectures to be clear and easy for AI to understand, with tests that verify everything is working as expected.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill ai-first-engineeringOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/pi/core/skills/ai-first-engineering/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
AI-First Engineering
Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.
Process Shifts
- Planning quality matters more than typing speed.
- Eval coverage matters more than anecdotal confidence.
- 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
FAQ
Why is planning more important now?
With AI writing code, the most important thing is to give the right instructions and ensure the final result is as expected, which comes from good planning.
What should I focus on when reviewing AI-written code?
Focus on ensuring the code behaves as it should, that it's secure, handles errors well, and won't cause problems when launched. Let automated tools handle things like formatting.
What kind of code should AI avoid generating?
Avoid code with hidden behaviors or unclear rules. Prefer code with well-defined parts, predictable functionality, and clear tests.
Details
Original description by the author
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.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #9
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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