A robust automation framework for tracking positions, scanning for opportunities, and executing trades on prediction markets like Kalshi and Polymarket.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prediction-market-watcher
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install prediction-market-watcher 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 Prediction Market Watcher?
The Prediction Market Watcher is an essential addition to the Openclaw Skills library, designed for traders who want to automate their interactions with Kalshi and Polymarket. By providing a structured way to monitor open positions and scan for high-value bets, this skill bridges the gap between raw market data and actionable trading strategies.
It leverages secure RSA-PSS signed requests for Kalshi and utilizes public APIs for Polymarket, ensuring a professional-grade integration for financial prediction markets. By integrating this into your agentic workflow, you can maintain a constant pulse on market shifts without manual oversight, utilizing Openclaw Skills to handle the heavy lifting of API communication and risk validation.
Prediction Market Watcher Use Cases
- Portfolio management and real-time position tracking on Kalshi markets
- Automated scanning for value bets across hundreds of active contracts
- Real-time Bitcoin and crypto price comparisons against strike prices using external data sources
- Automated settlement reminders and notifications via external messaging services like Telegram
- Enforcing strict, programmatic risk management rules to protect trading capital
How Prediction Market Watcher Works
- The skill initializes a secure connection using RSA private keys stored within the kalshi-agent directory.
- It fetches real-time market data from the Kalshi or Polymarket API endpoints through Openclaw Skills logic.
- The agent evaluates current market prices against defined strikes or rules (such as BTC price vs bet range).
- A local risk management layer checks risk_state.json to ensure the proposed trade falls within the daily budget and position limits.
- Orders are executed via signed POST requests to the trade API, or alerts are triggered for manual intervention via the CLI.
Prediction Market Watcher Setup
To integrate this into your environment, follow these steps within the Openclaw Skills framework:
- Create the required workspace directory:
mkdir -p ~/workspace/kalshi-agent
- Place your
config.jsonand RSA private key (kalshi.key) in thekalshi-agent/folder. - Ensure the
kalshi_client.pyandrisk.pyscripts are present to handle authentication and safety checks. - Initialize the environment and verify your balance by running a status check:
cd ~/workspace/kalshi-agent && python3 kalshi_agent.py --status
Prediction Market Watcher Data Schema & Taxonomy
The Prediction Market Watcher organizes data through the following file structure and metadata taxonomy to ensure Openclaw Skills can process them accurately:
| File/Object | Description |
|---|---|
config.json |
Contains API credentials and Key IDs for the Kalshi platform. |
kalshi.key |
The RSA private key required for signing trading requests via RSA-PSS. |
risk_state.json |
Tracks daily budget usage, open position counts, and confidence-based betting limits. |
Market Data |
Schema including fields like yes_bid_dollars, yes_ask_dollars, floor_strike, and close_time. |
Order Object |
Structured JSON for placing trades with limit_price (stored in cents) and contract count. |
Prediction Market Watcher Advanced Features
- Integrated Risk Engine: Automatically enforces a maximum $20 daily budget and tiered betting limits ranging from $1 to $5 based on Openclaw Skills logic.
- Automated Settlement Cron Jobs: Ability to schedule scripts that run 15 minutes before market close to notify users of settlement status.
- Multi-Market Scanning: Use the --scan flag to efficiently parse dozens of open markets to find trades meeting specific profitability criteria.
- Crypto-Price Comparison: Compare live CF Benchmarks RTI data against market floor and cap strikes to identify arbitrage or mispricing.
- Cross-Platform Read Support: Pull active market data from the Polymarket Gamma API for comparative market analysis.
SKILL.md
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