Agent Onboarding logo

Agent Onboarding

Organization
Nunchi-trade
agent-onboarding

First-time setup — from zero to first trade on Hyperliquid

Overview

PublisherNunchi-trade
Repositoryagent-cli
Skill nameagent-onboarding
Stars
521
Forks
136
Bundled files
Instructions only
LicenseMIT
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 Nunchi-trade on GitHub. Read the source before you install it.

Installation

Install the Agent Onboarding 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/Nunchi-trade/agent-cli/tree/main/skills/onboard
  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/Nunchi-trade/agent-cli.git /tmp/agent-cli
mkdir -p .claude/skills
cp -r /tmp/agent-cli/skills/onboard .claude/skills/agent-onboarding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Onboarding 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 Agent Onboarding 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 Agent Onboarding 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.

Agent Onboarding

Complete first-time setup for the YEX autonomous trading system on Hyperliquid. Follow every step in order. Do not skip steps. Verify each step before proceeding to the next.

Agent Mandate

You are onboarding onto the YEX trading system. Your goal is to reach a state where you can autonomously trade on Hyperliquid (testnet or mainnet). Every step has a verification command — if verification fails, fix the issue before continuing.

RULES:

  • Follow steps sequentially — each depends on the previous
  • NEVER skip builder fee approval — all orders will fail without it
  • ALWAYS validate with a mock trade before going live
  • NEVER start on mainnet without completing testnet validation first
  • Save all credentials (passwords, addresses) — they cannot be recovered

Step 1: Environment Bootstrap

Install Python dependencies and verify the CLI works.

Option A — Shell script (recommended):

bash
cd ~/agent-cli
bash scripts/bootstrap.sh

Option B — Manual:

bash
cd ~/agent-cli
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Verify:

bash
hl setup check

Expected: All items show OK, no !! issues (except missing key — that's Step 2).

If fails:

ErrorFix
python3 not foundInstall Python 3.10+ via brew install python or system package manager
pip install failedEnsure you're in a venv: source .venv/bin/activate
ModuleNotFoundError: hyperliquidRun pip install hyperliquid-python-sdk

Step 2: Wallet Setup

Create or import a Hyperliquid wallet.

Decision tree:

  • Do you have a private key?
    • YES → Import it:
      bash
      hl wallet import --key 0x<your_key>
    • NO → Create a new one (non-interactive, with auto-saved credentials):
      bash
      hl wallet auto --save-env --json
      This creates the wallet, saves HL_KEYSTORE_PASSWORD to ~/.hl-agent/env (auto-detected by CLI), and outputs JSON: {"address": "...", "password": "...", "keystore": "...", "env_file": "..."}.

If you used --save-env, the CLI auto-detects the password from ~/.hl-agent/env — no manual export needed.

If you did NOT use --save-env, set the keystore password manually:

bash
export HL_KEYSTORE_PASSWORD=<password>

Verify:

bash
hl wallet list

Expected: At least one address shown.

If fails:

ErrorFix
No keystores foundRun hl wallet auto
eth_account not installedRun pip install eth-account>=0.10.0

Step 3: Network Configuration

Choose testnet (default, recommended for first run) or mainnet.

Testnet (default):

bash
export HL_TESTNET=true

Mainnet:

bash
export HL_TESTNET=false

Verify:

bash
hl setup check

Expected: Network: testnet or Network: mainnet shown.


Step 4: Fund Account

CRITICAL: On testnet, you MUST claim USDyP tokens before ANY trading commands will work (including builder approval). Without funds, all orders fail silently.

Testnet — Claim USDyP

bash
hl setup claim-usdyp

If you get "Wallet not eligible" error: New wallets must connect to Hyperliquid testnet once before claiming.

  1. Visit https://app.hyperliquid-testnet.xyz
  2. Connect the wallet address shown by hl wallet list
  3. Re-run hl setup claim-usdyp

Verify:

bash
hl account

Expected: USDyP balance > 0. If balance is 0, do NOT proceed — all subsequent steps will fail.

Mainnet — Deposit USDC

Deposit USDC to your Hyperliquid sub-account manually via the Hyperliquid web UI. This cannot be automated.

Verify:

bash
hl account --mainnet

Expected: USDC balance > 0.


Step 5: Builder Fee Approval

Approve the builder fee so your orders include revenue collection. This is a one-time on-chain approval.

Testnet:

bash
hl builder approve

Mainnet:

bash
hl builder approve --mainnet

Verify:

bash
hl builder status

Expected: Builder fee: 10.0 bps -> 0x0D1DB1C8...

If fails:

ErrorFix
No private keyComplete Step 2
insufficient fundsComplete Step 4

Step 6: Validate with Mock Trade

Run a strategy in mock mode to verify the full pipeline without real orders.

bash
hl run avellaneda_mm --mock --fresh --max-ticks 3

Expected: 3 ticks execute, strategy produces decisions, no errors.

If fails:

ErrorFix
ModuleNotFoundErrorRun pip install -e . again
Strategy crashCheck traceback — likely missing dependency

Step 7: First Live Trade (Testnet)

Run a real strategy on testnet with a short time limit.

bash
hl run engine_mm -i ETH-PERP --tick 15 --max-ticks 5

Verify:

bash
hl status

Expected: Shows position or recent fills. Trades logged to data/cli/trades.jsonl.


Step 8: APEX Multi-Slot (Optional)

After single-strategy validation, try the full APEX orchestrator.

bash
hl apex run --mock --fresh --max-ticks 5

Then live:

bash
hl apex run --max-ticks 10

Step 9: Mainnet (When Ready)

Only after completing Steps 1-8 on testnet:

IMPORTANT: Switching from testnet to mainnet requires re-approving the builder fee on mainnet. Testnet approvals do NOT carry over.

Checklist

  1. Switch network:

    bash
    export HL_TESTNET=false
  2. Deposit USDC to your HL sub-account via the Hyperliquid web UI

  3. Verify balance:

    bash
    hl account --mainnet
  4. Approve builder fee on mainnet (required again — separate from testnet approval):

    bash
    hl builder approve --mainnet
  5. Test with a single strategy first:

    bash
    hl run engine_mm -i ETH-PERP --tick 15 --max-ticks 5 --mainnet
  6. Verify: hl status

Network Differences

TestnetMainnet
CurrencyUSDyP (free, claim via hl setup claim-usdyp)USDC (real money)
InstrumentsSame tickers (ETH-PERP, BTC-PERP, etc.)Same tickers
YEX marketsVXX-USDYP, US3M-USDYP, BTCSWP-USDYPSame instruments
Builder feeMust approve separatelyMust approve separately
--mainnet flagNot needed (default is testnet)Required on all commands
RiskNone (play money)Real financial risk

Common Mistakes

  • Forgot to re-approve builder on mainnet → orders fail. Run hl builder approve --mainnet.
  • No USDC deposited → orders fail with insufficient funds. Deposit first.
  • Using testnet env with --mainnet flag → confusing. Set HL_TESTNET=false instead of mixing flags.
  • Forgot --mainnet on a command → runs on testnet by accident (harmless but confusing).

Anti-Patterns

  • Skipping builder fee approval → Every order fails silently. Always approve first.
  • Going mainnet without testnet validation → Real money at risk with unverified setup.
  • Running APEX before single-strategy test → APEX composes multiple systems — if any sub-component fails, debugging is harder.
  • Ignoring password save → Keystore password cannot be recovered. Lose it = lose wallet access.
  • Not setting HL_KEYSTORE_PASSWORD → CLI can't auto-unlock keystore. Every command will fail.

Complete Environment Variables

VariableRequiredDescription
HL_KEYSTORE_PASSWORDYes*Password for encrypted keystore
HL_PRIVATE_KEYAlt*Raw private key (alternative to keystore)
HL_TESTNETNotrue (default) or false for mainnet
BUILDER_ADDRESSNoOverride builder fee address
BUILDER_FEE_TENTHS_BPSNoOverride fee rate (default: 100 = 10 bps)
ANTHROPIC_API_KEYNoFor claude_agent strategy
GEMINI_API_KEYNoFor claude_agent with Gemini

* Either keystore with HL_KEYSTORE_PASSWORD or HL_PRIVATE_KEY is required.

Composition

This skill is the entry point for all other skills. After completing onboarding:

  • Trade: hl run <strategy> — see strategies with hl strategies
  • APEX: hl apex run — multi-slot orchestrator
  • REFLECT: hl reflect run — nightly performance review
  • Radar: hl radar run — find trading opportunities
  • Guard: hl guard run -i <instrument> — trailing stop protection

Frequently asked questions

What does the Agent Onboarding AI skill do?

First-time setup — from zero to first trade on Hyperliquid

Why use Agent Onboarding on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Nunchi-trade/agent-cli/tree/main/skills/onboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Onboarding?

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 Agent Onboarding?

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

Is the Agent Onboarding AI skill free?

Yes. It is published on GitHub by Nunchi-trade under the MIT license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇