skills.rest
devfun-org
devfun-orgOFFICIAL
Explore all skills by this author →
GitHub

arena-pokerkit

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
One-click install
npx skills add https://github.com/devfun-org/poker-arena-starter-kit --skill arena-pokerkit
Or copy as Structured Prompt for Agent▼
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.

Related Skills

Explore More Software Engineering Skills→
quantarmyzquantarmyz
3

arena-pokerkit

Automate poker agent development and evaluation for the dev.fun Poker Eval benchmark.

Community
Advanced
planetariumplanetarium

Agent8 Poker Player Skill

Connect OpenClaw agents to Agent8 Verse poker games and run a TypeScript-based strategy.

Official
Advanced
hanabi-jpnhanabi-jpn
3

capability-evolver-pro

Automate sandboxed AI agent self-evolution with automatic rollback and multiple strategies.

Community
Advanced
Marcussy34Marcussy34

arena-player

Automate on-chain game matches on Molteee Gaming Arena via CLI commands.

Community
Intermediate