arena-pokerkit

Name: arena-pokerkit
Availability: InStock
Author: dev.fun

Automate development, validation, and live evaluation of poker agents for the Arena API.

20|9|Updated May 25, 2026

Search Tags:#automation #ai #agent #benchmark #reinforcement-learning #poker

Ready to run

npx skills add https://github.com/devfun-org/poker-arena-starter-kit --skill arena-pokerkit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arena-pokerkit
Source: https://github.com/devfun-org/poker-arena-starter-kit/tree/main
Command: npx skills add https://github.com/devfun-org/poker-arena-starter-kit --skill arena-pokerkit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, python-dotenv, treys, pokerkit, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the complex process of developing, testing, and benchmarking poker-playing agents against professional-grade reference bots on the dev.fun Arena platform.

Core Features & Use Cases

End-to-End Dev Loop: Provides a unified interface for local iteration, unit testing, and live Arena evaluation.
Heuristic Learning: Enables data-driven improvement of agent decision-making through failure analysis and strategy refinement.
Use Case: A developer can use this Skill to iteratively refine a poker agent's decide function by analyzing failure reports from previous matches and running local self-play simulations to verify performance gains before submitting to the leaderboard.

Quick Start

Use the arena-pokerkit skill to build a new poker bot and start the guided onboarding process.

Frequently Asked Questions about arena-pokerkit

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

FAQPage Schema

How do I build and test an AI poker bot locally?▼

To build and test an AI poker bot locally, use this Skill to implement heuristic decision logic, run local self-play simulations, and verify performance gains before submitting to a live leaderboard.

What is the best way to automate poker agent evaluation against reference bots?▼

Automating poker agent evaluation involves integrating with the Arena API to manage agent registration, execute matches against professional-grade reference bots, and collect automated failure analysis reports for iterative optimization.

Can I use Python to create a reinforcement learning poker agent for benchmarking?▼

You can use Python 3.11+ and the pokerkit dependency to develop reinforcement learning poker agents, leveraging heuristic-based decision logic and data-driven strategy refinement for benchmark competitions.

How does automated failure analysis improve poker bot decision-making?▼

Automated failure analysis improves poker bot decision-making by parsing match failure reports from previous Arena matches, allowing developers to iteratively refine the agent's decide function and validate improvements through local self-play.

Do I need Python 3.11 to run the pokerkit benchmark integration?▼

Yes, Python 3.11 or higher is required to run this Skill and integrate with the pokerkit library and Arena API for agent registration, match execution, and leaderboard submission.