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Frontend Playbook

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austintgriffith
frontend-playbook

Use when working inside a Scaffold-ETH 2 app: running it against a local chain or a fork of a real chain, demoing on that fork, or shipping it as a static export to IPFS or ENS. Not for rehearsing or running a live contract deployment (`/orchestration`).

Overview

Publisheraustintgriffith
Repositoryethskills
Skill namefrontend-playbook
Stars
298
Forks
64
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by austintgriffith on GitHub. Read the source before you install it.

Installation

Install the Frontend Playbook AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/austintgriffith/ethskills/tree/master/frontend-playbook
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/austintgriffith/ethskills.git /tmp/ethskills
mkdir -p .claude/skills
cp -r /tmp/ethskills/frontend-playbook .claude/skills/frontend-playbook
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Playbook in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Frontend Playbook on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Frontend Playbook is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Frontend Playbook

Follow this workflow; do not treat a green build or successful upload as proof that the intended app shipped.

Start with the requested stack

When the user wants Scaffold-ETH 2 or has not selected a stack for a conventional contract-plus-frontend dApp, use its generator:

bash
npx create-eth@2.0.23 # Tested version; update deliberately

Use the generated Foundry/Next.js monorepo, wallet integration, contract hooks, and components. Respect an explicitly requested alternative stack.

Choose the local chain deliberately

  • Use yarn chain for isolated contracts, mocks, and unit tests.
  • Use yarn fork --network <chain> when behavior depends on deployed protocols, tokens, balances, or other real chain state.
  • In fork mode, point the frontend at the local Anvil network (chains.foundry, chain ID 31337), not the upstream chain being copied. Switch to the real target chain only for a real deployment.

A fork also grants powers the real chain does not. Fund demo identities from state that already exists rather than deploying a mock token:

bash
cast rpc anvil_impersonateAccount <whale>
cast send <token> "transfer(address,uint256)" <demo-account> <amount> \
  --from <whale> --unlocked

anvil_setBalance and anvil_setStorageAt are equivalent overrides when no suitable holder exists. Nothing here is broadcast; the fork is a local copy.

Anvil normally mines only when a transaction arrives, so between transactions the latest block and block.timestamp remain frozen; the next transaction advances time in one jump. This silently breaks live deadlines, expiry, and vesting displays even when vm.warp unit tests pass. For continuous behavior, enable interval mining:

bash
cast rpc anvil_setIntervalMining 1

For a frozen live demo, enable interval mining. Mining a single block with evm_mine restamps block.timestamp once and it freezes again immediately; manual mining and evm_increaseTime are for controlled single-step tests, not a running demo. Add --block-time 1 to the fork script when the project should always run that way.

Build a static IPFS release

Configure the IPFS build for static export, route directories, and static images:

typescript
if (process.env.NEXT_PUBLIC_IPFS_BUILD === "true") {
  nextConfig.output = "export";
  nextConfig.trailingSlash = true;
  nextConfig.images = { unoptimized: true };
}

Set the production origin before building so Open Graph URLs and images do not resolve to localhost. Then remove old artifacts and rebuild:

bash
cd packages/nextjs
rm -rf .next out
NEXT_PUBLIC_PRODUCTION_URL="https://<production-domain>" \
  NEXT_PUBLIC_IPFS_BUILD=true \
  yarn build

On Node 25, the built-in localStorage global can exist without the standard Web Storage methods when no backing file is configured; libraries detect it and crash on calls such as getItem() during static prerender. Apply a process-level remedy inherited by build workers, not only code in instrumentation.ts or next.config.ts. Depending on the app, use one of:

bash
NODE_OPTIONS="--localstorage-file=.node-localstorage"
NODE_OPTIONS="--no-experimental-webstorage"

Verify before and after upload

Before uploading, confirm the expected change is in out/, that every route emitted its own directory, and that generated metadata carries the production origin:

bash
ls out/*/index.html # One directory with an index.html per route

After uploading:

  1. Record the CID. An unchanged CID means the uploaded bytes are identical; check the build output and upload target before blaming gateway caching.

  2. Load the root and at least one non-home route through the gateway. Root success does not prove exported routes resolve.

    bash
    curl -s -o /dev/null -w "%{http_code}" -L "https://<gateway>/ipfs/<cid>/debug/" # Expect 200, not 404
  3. Verify the Open Graph image and URL use the production origin.

  4. For ENS, update the content hash only after the deployed CID has been reviewed and approved, then confirm the resolver and public gateway serve that CID.

Do not claim the release is complete until these checks pass.

Frequently asked questions

What does the Frontend Playbook AI skill do?

Use when working inside a Scaffold-ETH 2 app: running it against a local chain or a fork of a real chain, demoing on that fork, or shipping it as a static export to IPFS or ENS. Not for rehearsing or running a live contract deployment (`/orchestration`).

Why use Frontend Playbook on TypingMind?

Because you install it once and use it with any model. Frontend Playbook is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Frontend Playbook in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/austintgriffith/ethskills/tree/master/frontend-playbook. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Frontend Playbook?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Frontend Playbook?

As many as you like. As long as a model supports skills, you can use Frontend Playbook with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Frontend Playbook AI skill free?

It is published on GitHub by austintgriffith. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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