I developed KALARB, a Python-based arbitrage scanner that continuously analyzes prediction markets across multiple platforms—including Kalshi, Polymarket, and Manifold—to identify pricing inefficiencies and potential arbitrage opportunities. The system retrieves live market data through platform APIs, normalizes contract prices, and applies algorithms to detect both internal spread arbitrage (within a single market) and cross-platform arbitrage opportunities where the combined cost of opposite outcomes is below $1.00. To improve detection of equivalent events across platforms, the program uses fuzzy string matching to compare market titles and identify related contracts. The tool also evaluates trading fees, estimates net profitability, and generates investment recommendations under different budget constraints, allowing users to quickly assess whether a trade remains profitable after transaction costs.