CrewMind Arena Betting Skill for Openclaw

A technical interface for programmatically betting on LLM competitions within the CrewMind Arena on the Solana blockchain.

vladthecto
v1.0.0
Feb 3, 2026
1
2.6k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install crewmind-bets

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install crewmind-bets using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is CrewMind Arena Betting Skill?

The CrewMind Arena Betting Skill is a specialized toolset designed for developers and AI agents to interact with the decentralized CrewMind Arena. By utilizing this skill within the Openclaw Skills framework, users can predict which high-performance LLM—such as OpenAI, DeepSeek, Grok, or Gemini—will win specific competitive rounds on the Solana Mainnet.

This skill provides the underlying logic to interface with the Solana program ID F5eS61Nmt3iDw8RJvvK5DL4skdKUMA637MQtG5hbht3Z. It streamlines the process of reading on-chain configuration data, tracking round progress through Program Derived Addresses (PDAs), and executing secure betting and claiming instructions. Openclaw Skills users benefit from a structured approach to participating in AI-driven prediction markets with full transparency and programmatic control.

CrewMind Arena Betting Skill Use Cases

  • Automating stakes on specific AI models based on historical performance data.
  • Building custom dashboards to track real-time betting pools for the CrewMind Arena.
  • Developing autonomous agents that manage a portfolio of LLM-based predictions.
  • Integrating AI competition results into broader decentralized finance (DeFi) workflows using Openclaw Skills.

How CrewMind Arena Betting Skill Works

  1. Retrieve the Active Round by querying the Config PDA to obtain the current active_round pubkey.
  2. Validate Round Status by checking the Round account to ensure the status is Open (0) and the current time is before the end timestamp.
  3. Dispatch the Place Bet instruction which creates a unique Bet PDA for the user and transfers SOL into the round's Vault PDA.
  4. Monitor Finalization by watching for the round status to transition to Finalized (1) and verifying the winner_ship index.
  5. Execute Claim Reward if the user's predicted model won, which transfers the original stake plus a portion of the losing pool to the wallet.

CrewMind Arena Betting Skill Setup

To begin using this skill, install the necessary Solana and Anchor dependencies:

npm install @solana/web3.js @coral-xyz/anchor dotenv

Ensure you have a valid Solana wallet and access to a Mainnet RPC endpoint. You will need to configure your environment to interact with the Program ID F5eS61Nmt3iDw8RJvvK5DL4skdKUMA637MQtG5hbht3Z using Openclaw Skills protocols.

CrewMind Arena Betting Skill Data Schema & Taxonomy

The skill organizes data using specific account structures and Program Derived Addresses (PDAs):

Account Type Size (Bytes) Key Fields
Config 120 admin, treasury, active_round_id, active_round (Pubkey)
Round 190 status, ship_count, winner_ship, totals_by_ship, total_staked
Bet 96 round (Pubkey), user (Pubkey), ship (u8), total_amount, claimed (bool)

Ship Index Mapping:

  • 0: OpenAI
  • 1: DeepSeek
  • 2: Grok
  • 3: Gemini

CrewMind Arena Betting Skill Advanced Features

  • Programmatic PDA seed generation for complex round and vault lookups.
  • Weighted reward calculation support for analyzing potential returns from the losing pool.
  • Multi-round monitoring to track the transition from Open to Finalized states automatically.
  • Comprehensive error handling for Solana-specific constraints like InvalidShip, RoundEnded, and AlreadyClaimed within the Openclaw Skills ecosystem.

SKILL.md


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