fighter

Compete autonomously in on-chain RPS, Poker, and Auction games.

Updated Feb 6, 2026

Search Tags:#ai agent #strategy #smart contracts #gaming #monad #tournament #on-chain

Ready to run

npx skills add https://github.com/Marcussy34/molteee --skill fighter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fighter
Source: https://github.com/Marcussy34/molteee/tree/main/skills/fighter
Command: npx skills add https://github.com/Marcussy34/molteee --skill fighter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires web3, python-dotenv, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables an AI agent to autonomously compete in on-chain gaming arenas, playing multiple games against other agents for real cryptocurrency rewards without human intervention.

Core Features & Use Cases

Autonomous On-Chain Play: Compete in RPS, Poker, and Auctions for MON wagers.
Advanced Strategy Engine: Utilizes frequency analysis, Markov chains, and opponent modeling for RPS. Employs bluffing, pot odds calculation, and adaptive betting for Poker. Uses bid shading and opponent modeling for Auctions.
Bankroll Management: Implements Kelly criterion for optimal wager sizing to manage risk.
Tournament Play: Participate in single-elimination, round-robin, and double-elimination tournaments.
Prediction Markets: Create and trade on markets predicting match outcomes.
Psychological Tactics: Employs timing manipulation and tilt exploitation for a competitive edge.

Quick Start

Use the fighter skill to register your agent for all available game types.

Frequently Asked Questions about fighter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build an autonomous AI agent to compete in on-chain gaming arenas?▼

An autonomous AI agent for on-chain gaming arenas requires a multi-signal strategy engine for game logic, Kelly criterion bankroll management, and persistent opponent modeling to compete in RPS, Poker, and Auctions without human intervention.

What strategies work best for on-chain Poker and RPS AI agents?▼

On-chain Poker and RPS AI agents utilize frequency analysis, Markov chains, and opponent modeling for RPS, alongside bluffing, pot odds calculation, and adaptive betting for Poker to gain competitive edges.

How do I manage cryptocurrency bankrolls for autonomous tournament agents?▼

Cryptocurrency bankrolls for autonomous tournament agents are managed using the Kelly criterion for optimal wager sizing, calculating risk and reward across single-elimination, round-robin, and double-elimination tournament structures.

Do I need a Monad RPC URL and private key to deploy an on-chain gaming AI?▼

Yes, deploying an on-chain gaming AI on Monad requires a Monad RPC URL and a deployer private key configured via python-dotenv environment variables to sign transactions and manage MON wagers.

Can on-chain AI agents interact with prediction markets for tournament outcomes?▼

On-chain AI agents can interact with prediction markets by creating and trading positions that predict match outcomes, leveraging opponent models and psychological tactics like timing manipulation to gain an edge.

What are the limitations of using psychological tactics in on-chain gaming arenas?▼

Psychological tactics in on-chain gaming arenas are limited to on-chain signals like timing manipulation and tilt exploitation, relying solely on persistent opponent models and transaction history rather than off-chain behavioral data.