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Orchestration

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austintgriffith
orchestration

Use when a Scaffold-ETH 2 build leaves localhost: planning the launch order, deploying and verifying contracts on a live network, standing up a fork of a real chain to rehearse a deploy, or fixing a contract bug that is already onchain. Not for running the app or a demo against that fork (`/frontend-playbook`).

Overview

Publisheraustintgriffith
Repositoryethskills
Skill nameorchestration
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 Orchestration 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/orchestration
  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/orchestration .claude/skills/orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Orchestration 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 Orchestration 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 Orchestration 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.

Shipping a dApp

Going live is three moves, never one: contracts local, then contracts live with the frontend still on localhost, then the frontend public. The middle move is the one that gets skipped and the one that catches everything — real chain, real gas, real decimals, a real wallet — while the UI is still yours alone to edit in seconds.

Name a go/no-go condition at each boundary, not just the commands. Three of them:

  • Before deploying to the live network: contract tests pass, the deploy script runs clean against a local fork, and there is a funded deployer — yarn generate creates one (Foundry: an encrypted keystore in ~/.foundry/keystores; Hardhat: an encrypted key in packages/hardhat/.env), yarn account prints its address and its balance on each chain, and it needs real ETH on the target chain before the deploy, not after it fails.
  • Before the frontend is reachable publicly: you have walked the entire user journey against the live contracts with a real wallet and real money — $1-10 of your own — and every step worked.
  • After the frontend deploy: you have loaded the public URL yourself and put one transaction through it.

A runbook that names the commands but not the conditions will keep going after something has already gone wrong.

Verify in the same breath as deploy

yarn verify --network base (or your target chain) belongs immediately after yarn deploy --network base, not in a launch checklist weeks out. Until it runs, users and integrators are looking at opaque bytecode, and you are debugging a live contract without source on the explorer.

Nobody has to go and get a block explorer API key first. packages/foundry/.env.example already carries a working ETHERSCAN_API_KEY and postinstall copies it to .env, so yarn verify works on a fresh checkout. So a launch plan never lists "obtain an explorer key" among its steps, and never hands anyone an env template with a blank ETHERSCAN_API_KEY= to fill in: the value is already there, and a step that waits on that key waits for nothing. Swapping in your own is optional housekeeping afterwards, not a launch dependency. One real constraint: yarn verify replays broadcast/run-latest.json, so run it from the checkout that did the deploy.

A live contract bug goes back to the start

Deployed bytecode cannot be edited, and the contract is a public API — a guard added in the frontend binds nobody, because direct calls, integrators and other frontends reach the same function. Shipping the UI clamp to buy hours is fine; calling it the fix is not.

The fix is the whole loop, in order: reproduce locally, correct the source, add the regression test that fails without the correction, redeploy (or upgrade in place, if it is behind a proxy), repoint the frontend if the address moved, and handle the state and users already there — migration or comms, your call. The loop is the same whether the bug surfaces before launch or with the app already public.

SE-2 specifics worth having right

  • Fork the chain you are targeting rather than yarn chain — yarn fork --network base gives you Uniswap, USDC, Aave and funded whales already deployed, so you write no mocks of things that exist. Two tokens, and that is the whole rule: yarn binds the first token after the script name to $0, whatever it looks like, so the script's $1 is only set if you pass something before the value. yarn fork base and yarn fork --network=base both leave $1 empty and silently fork Ethereum mainnet; yarn fork --network base, yarn fork -n base and yarn fork -- base all work. In the hardhat flavor yarn fork takes no network argument (passing one errors) and always forks mainnet — change forking.url in hardhat.config.ts to fork anything else. Either way the fork answers chain id 31337, so the chain id never tells you what you forked: check for state only the target chain has, such as code at one of its token addresses.
  • scaffold.config.ts is committed. An RPC or API key pasted into rpcOverrides or alchemyApiKey is a published key; read it from process.env and keep the value in .env.local. wallets/SKILL.md covers the rest of key handling.
  • The frontend goes to the live chain only when you say so. scaffold.config.ts targetNetworks still names the local chain after the contracts are live, and a frontend built before that switch reads a chain nobody is on. Change it in the same step that repoints the frontend at the deployed addresses, and not before — during fork work it belongs on chains.foundry, since the fork answers 31337 whatever it forked (frontend-playbook has the detail).
  • The burner wallet stays local. burnerWalletMode in scaffold.config.ts defaults to "localNetworksOnly". On a live chain "allNetworks" gives users a key stored in localStorage; keep the default for production.
  • A frontend-only ticket deploys nothing. Do not stand up a chain and redeploy to regenerate deployedContracts.ts unless deploying was the ask.

Frequently asked questions

What does the Orchestration AI skill do?

Use when a Scaffold-ETH 2 build leaves localhost: planning the launch order, deploying and verifying contracts on a live network, standing up a fork of a real chain to rehearse a deploy, or fixing a contract bug that is already onchain. Not for running the app or a demo against that fork (`/frontend-playbook`).

Why use Orchestration on TypingMind?

Because you install it once and use it with any model. Orchestration 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 Orchestration in TypingMind?

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

Which AI models can use Orchestration?

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 Orchestration?

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

Is the Orchestration 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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