An AI-powered market intelligence tool for researching prediction markets, analyzing betting odds, and identifying arbitrage opportunities.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install alpha-finder
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install alpha-finder using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Alpha Finder is a professional-grade market oracle designed to provide deep intelligence for prediction market enthusiasts and traders. By utilizing this capability within the Openclaw Skills ecosystem, users can access real-time probability assessments across major platforms like Polymarket and Kalshi. It bridges the gap between raw market data and actionable insights by aggregating information from social sentiment, technical repositories, and traditional news sources.
This skill is particularly valuable for users who need to identify market inefficiencies or conduct thorough due diligence before placing high-stakes bets. Operating on the Base network, it uses the x402 protocol to facilitate cost-effective research requests, ensuring that every analysis is backed by comprehensive data from the web, GitHub, Reddit, and X. Integrating Alpha Finder into your Openclaw Skills workflow allows for a more data-driven approach to prediction market participation.
To use this tool effectively within your Openclaw Skills setup, follow these configuration steps:
# Option 1: Set the private key as an environment variable
export X402_PRIVATE_KEY="0x..."
# Option 2: Create a configuration file in your home directory (Recommended)
echo '{"private_key": "0x..."}' > ~/.x402-config.json
# Run a research query
scripts/analyze.sh "Analyze Polymarket odds on AI developments"
The Alpha Finder skill organizes intelligence data into a structured format for easy interpretation:
| Attribute | Description |
|---|---|
| Source Platform | Origin of the data (e.g., Kalshi, Polymarket, Reddit) |
| Probability | AI-calculated percentage likelihood of an event outcome |
| Sentiment Score | Quantitative assessment of market discussion tone |
| Arbitrage Alert | Flags indicating price discrepancies between markets |
| Metadata | Includes links to GitHub repos or X threads for further verification |
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