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Kraken Fee Optimization

Organization
krakenfx
kraken-fee-optimization

Minimize trading fees through maker orders, volume tiers, and fee-aware execution.

Overview

Publisherkrakenfx
Repositorykraken-cli
Skill namekraken-fee-optimization
Stars
754
Forks
104
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 krakenfx on GitHub. Read the source before you install it.

Installation

Install the Kraken Fee Optimization 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/krakenfx/kraken-cli/tree/main/skills/kraken-fee-optimization
  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/krakenfx/kraken-cli.git /tmp/kraken-cli
mkdir -p .claude/skills
cp -r /tmp/kraken-cli/skills/kraken-fee-optimization .claude/skills/kraken-fee-optimization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kraken Fee Optimization 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 Kraken Fee Optimization 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 Kraken Fee Optimization 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.

kraken-fee-optimization

Use this skill for:

  • understanding maker vs taker fee structures
  • using post-only orders to guarantee maker rates
  • tracking 30-day volume for tier progression
  • choosing fee currency to minimize cost

Fee Tiers

Check current 30-day volume and fee rates:

bash
kraken volume --pair BTCUSD -o json 2>/dev/null

The response includes volume (30-day USD equivalent) and fees / fees_maker arrays showing the tier schedule.

Maker vs Taker

  • Taker: you remove liquidity (market orders, limit orders that fill immediately). Higher fee.
  • Maker: you add liquidity (limit orders that rest on the book). Lower fee, sometimes zero.

At starter tier: 0.26% taker, 0.16% maker. The difference compounds over many trades.

Post-Only Orders

Force maker execution by rejecting orders that would immediately fill:

bash
kraken order buy BTCUSD 0.001 --type limit --price 50000 --oflags post -o json 2>/dev/null

If the order would cross the spread and fill as taker, it is rejected instead. This guarantees maker fees on every fill.

Fee Currency Selection

By default, fees are deducted from the received currency. Override with:

bash
kraken order buy BTCUSD 0.001 --type limit --price 50000 --oflags fciq -o json 2>/dev/null
FlagFee taken from
fciqQuote currency (e.g., USD)
fcibBase currency (e.g., BTC)

Paying fees in quote currency preserves your base asset balance, useful when accumulating.

Volume-Based Optimization

Higher 30-day volume unlocks lower fees. Track progress:

bash
kraken volume -o json 2>/dev/null

If close to the next tier threshold, consolidating trading activity (rather than splitting across exchanges) can push you into a cheaper bracket.

Futures Fee Schedules

bash
kraken futures feeschedules -o json 2>/dev/null

Futures fee volumes:

bash
kraken futures fee-schedule-volumes -o json 2>/dev/null

Fee-Aware Order Sizing

When buying a fixed dollar amount, account for fees in the volume calculation:

bash
# $100 buy at starter taker rate (0.26%)
# Effective spend: $100 / 1.0026 = $99.74 worth of BTC
# Fee: ~$0.26

For precise accounting, check the fee field in the trade response after execution.

Batch Orders for Fee Efficiency

Batch orders share a single API call, reducing overhead. Use when placing related orders:

bash
kraken order batch orders.json --pair BTCUSD -o json 2>/dev/null

Hard Rules

  • Order placement commands are dangerous. Never execute without explicit human approval.
  • Always --validate before submitting live orders.
  • Post-only orders can be rejected; handle rejection gracefully and retry.
  • If you hit a mismatch between what you are trying to do and the CLI's interface or responses — including a mismatch between this skill and the installed CLI version's contract — feel free to submit feedback with kraken feedback.

Frequently asked questions

What does the Kraken Fee Optimization AI skill do?

Minimize trading fees through maker orders, volume tiers, and fee-aware execution.

Why use Kraken Fee Optimization on TypingMind?

Because you install it once and use it with any model. Kraken Fee Optimization 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 Kraken Fee Optimization in TypingMind?

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

Which AI models can use Kraken Fee Optimization?

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 Kraken Fee Optimization?

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

Is the Kraken Fee Optimization AI skill free?

Yes. It is published on GitHub by krakenfx 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.

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