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Related Market Spread

Organization
Superior-Trade
related-market-spread

Use when two Polymarket markets imply different probabilities for a linked outcome.

Overview

PublisherSuperior-Trade
Repositorysuperior-skills
Skill namerelated-market-spread
Stars
215
Forks
9
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 Superior-Trade on GitHub. Read the source before you install it.

Installation

Install the Related Market Spread 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/Superior-Trade/superior-skills/tree/main/skills/related-market-spread
  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/Superior-Trade/superior-skills.git /tmp/superior-skills
mkdir -p .claude/skills
cp -r /tmp/superior-skills/skills/related-market-spread .claude/skills/related-market-spread
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Related Market Spread 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 Related Market Spread 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 Related Market Spread 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.

Strategy: Polymarket · Related-Market Spread

When to use

Use this when the user asks for relative-value trades, discrepancy checks, linked outcomes, or questions like "this market is mispriced versus that one."

What the agent should look for

  • At least two canonical Polymarket markets from GET /context/markets?venue=polymarket.
  • Resolution logic that is genuinely comparable across the two markets.
  • Spread between filled-trade prices that is wide versus historical behavior.
  • Enough historical filled data on both markets for the requested backtest window.
  • Ability to enter and exit both legs (or acknowledge unavailable liquidities before entering).

Backtest fit with filled data

Partial fit. Backtests built from filled TradeTick data are useful for checking whether the spread relationship has widened and then compressed in history. They cannot guarantee simultaneous fills or full cross-book liquidity.

Use this as a generated archetype: the strategy should be adapted in code before use, not treated as a finished edge.

Strategy logic

Track the latest filled price for both legs. Enter when spread is above a threshold and exits when spread narrows or max holding duration expires.

Nautilus strategy shape

  • Subscribe to TradeTick for both instruments.
  • Keep the latest filled price per instrument.
  • Compute spread each tick using the latest values.
  • Enter and flatten only when spread thresholds are hit.
  • Manage stale-state safety (e.g., skip entries until both legs have recent fills).

Example strategyConfig

json
{
  "primary_instrument_id": "AUTO_FROM_MARKET_A",
  "secondary_instrument_id": "AUTO_FROM_MARKET_B",
  "spread_entry": 0.08,
  "spread_exit": 0.03,
  "order_size": 10,
  "max_holding_ticks": 120
}

Iteration knobs

KnobEffect
spread_entryHigher = only trade stronger relative-value discrepancies.
spread_exitLower = wait for fuller normalization before exiting.
max_holding_ticksLower = tighter time risk control if convergence does not happen.

Failure modes

  1. Markets with different outcome mappings can produce a real structural spread, not an inefficiency.
  2. Resolution rules can shift, changing what "discrepancy" means.
  3. One leg may look tradable in backtest and be unavailable or illiquid live.
  4. TradeTick replay cannot prove simultaneous execution across legs.

User-facing framing

"This is a relative-value archetype, not a guaranteed edge. It is built from filled TradeTick backtests and exact market slug discovery first, then adapted into custom NautilusTrader code before any deployment."

Frequently asked questions

What does the Related Market Spread AI skill do?

Use when two Polymarket markets imply different probabilities for a linked outcome.

Why use Related Market Spread on TypingMind?

Because you install it once and use it with any model. Related Market Spread 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 Related Market Spread in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Superior-Trade/superior-skills/tree/main/skills/related-market-spread. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Related Market Spread?

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 Related Market Spread?

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

Is the Related Market Spread AI skill free?

Yes. It is published on GitHub by Superior-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.

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