product-recommendation

What problem does it solve? Bank employees need to recommend the right financial product for each customer, but comparing rates, fees, eligibility rules, and customer context manually is slow and error-prone. This Skill produces a grounded, segment-aware recommendation by combining customer data with verified product conditions. ## Core Features & Use Cases - Customer Context Analysis: Reads the customer's segment, holdings, net worth, and spending behavior via read-only customer data tools to frame the actual need. - Grounded Product Comparison: Pulls interest rates, fees, minimum deposits, notice periods, and eligibility from product data sources, citing each figure with its source file and section. - Structured Recommendation Output: Delivers a short comparison table followed by a single primary recommendation, rationale, and a runner-up with conditions for when it would be preferable. - Use Case: An employee asks whether a GrowthSaver or FixedDeposit Plus suits a customer with €20k sitting in a zero-interest current account, and receives a cited comparison plus a clear recommendation. ## Quick Start Ask which savings product to recommend for a specific customer and let the Skill gather the customer context and product conditions to produce a grounded recommendation.