0. AI Tools & Ethereum Skills

Goal

Build an AI-native Ethereum learning workflow and understand how to use an AI agent as a development assistant instead of an unchecked source of code.

Module Guide

This module is a method, not a topic. It runs across the whole course rather than sitting in a fixed slot: the habits you set up here — how you prompt an AI agent, what you let it do, and what you never let it do — carry into every lab and every contract you write afterward. The premise is simple: modern Ethereum development happens alongside AI tools, and the developers who benefit are the ones who treat AI as a knowledgeable but fallible assistant, not as an oracle that ships code straight to a chain holding real money.

The first half is about giving AI the right context. A general model knows some Ethereum, but it hallucinates APIs, mixes up standards, and writes plausible-looking but unsafe contracts. You fix this by pointing it at authoritative sources before you ask it anything: have it read the ethskills SKILL.md for a structured map of the stack, and feed it the Scaffold-ETH 2 llms-full.txt so its answers match the tools you actually use. Grounded this way, an AI agent becomes genuinely useful for explaining concepts, generating tests, debugging errors, and helping with a first-pass security review.

The second half is about judgment — knowing where AI stops. It cannot be trusted with private key handling, permission and access design, security sign-off, or the decision to deploy real funds. These are exactly the areas where a confident wrong answer is most dangerous, and where the immutability of the chain means mistakes don’t get patched. The recurring discipline this course asks for is: use AI to move faster, then verify its output against the fundamentals you learn in Modules 1–5. When an AI review flags a bug, you should know enough to confirm it’s real; when it writes a contract, you should know enough to spot what it got wrong.

The related lab is optional, ungraded practice: you’ll ask an AI agent to explain a stack for a dApp idea, produce a project risk checklist, and turn Scaffold-ETH docs into setup steps. There’s nothing to submit — the point is to build the workflow you’ll rely on for the rest of the course.

Topics

• How to ask AI to read ethskills SKILL.md first
• How to use the Scaffold-ETH 2 llms-full.txt
• ethskills modules: Ship, Why Ethereum, Protocol, Gas, Wallets, L2s, Standards, Tools, Money Legos, and Orchestration
• What AI can help with: explaining concepts, generating tests, debugging errors, and supporting security review
• What AI cannot replace: private key handling, security judgment, permission design, and real-fund deployment decisions

Lab Tasks

• Ask AI to use ethskills to explain the stack for a dApp idea
• Ask AI to generate a project risk checklist
• Ask AI to read the Scaffold-ETH 2 documentation and produce project setup steps

The related lab is optional, ungraded practice and does not require a submission.