القائمة
Use when an employee asks which product to recommend to a customer, or to compare products for a specific customer need. Produces a grounded, segment-aware recommendation.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Operations runbook for the agentic-banking-ecosystem repo. USE THIS SKILL when the user asks to deploy, build, provision, generate data, index, ingest, register, or clean up any part of the project — including infrastructure (azd), container images, Container Apps, the customer/product MCP servers, AI Search indexes, Foundry toolboxes, and the banking agents. Covers the full lifecycle: provision → generate data → build → deploy MCP → register toolboxes → index → ingest → deploy agents → clean up.
Use when an agent asks what data is required for a customer, account or transaction to be compliant — an onboarding checklist, an eligibility gap analysis, or "what do we still need to collect". Maps each compliance rule onto the concrete banking data model and returns an extensive, field-level requirements breakdown.
Use before asking Bank North's Compliance agent (the ask_compliance tool) any regulatory, KYC/AML, sanctions, fraud or product-eligibility question. Gathers the signed-in customer's real data first, then composes a precise, self-contained compliance question so the answer is specific to this customer and product rather than generic.
Use when the customer wants to order a product (e.g. open a savings account or apply for a credit card) or change their contact details. Enforces the human-in-the-loop confirmation and compliance guardrails.
Use when the agent should proactively spot an optimisation for the signed-in customer (e.g. a large uninvested balance on the current account) and offer help. Always asks permission first, then gives two concrete, personalised recommendations, and only acts human-in-the-loop.
Use when the customer asks about their balance, transactions, or personal details. Produces a grounded, confidential answer from the customer data MCP tools.
| name | product-recommendation |
| description | Use when an employee asks which product to recommend to a customer, or to compare products for a specific customer need. Produces a grounded, segment-aware recommendation. |
Help an employee recommend the most suitable financial product for the customer in front of them, grounded in product conditions and the customer's context.
get_net_worth for the customer's balance picture across all accounts
(e.g. spotting a large sum sitting on a low/zero-interest current account
that could be moved to a savings product).summarize_spending to ground the recommendation in the customer's
actual spending behaviour (top categories and merchants) — e.g. a frequent
traveller or high card-spend profile that suits a particular card.search_financial_products and the product
data tools to pull interest rates, fees, minimum deposits, notice periods and
eligibility for the candidate products.A short comparison (product, rate, fee, min deposit, key condition) followed by the recommendation, rationale and sources.