---
name: al-637-one-chat-agent-for-both-your-sales-spreadsheet-and-your-s
description: "An AWS Machine Learning Blog how-to (Nov 3, 2026) shows Amazon Quick combining an enriched sales dataset (text-to-SQL) with an S3 knowledge base of business PDFs inside one Space, then linking a custom chat agent that answers across both with citations. Example questions span “How did sales change by region?” and “How do 2025 results align with our market analysis memo?” The walkthrough covers semantic column descriptions, custom dataset instructions, Spaces, persona instructions, sharing with least-privilege access, and generating PowerPoint/Excel/Word from chat. Needs an Amazon Quick Enterprise subscription for dataset creation; adapt the same pattern on any stack that can join warehouse metrics with a document store behind one agent. Use when the user wants help with structured plus unstructured business chat agent (research) for a small business."
---

# One chat agent for both your sales spreadsheet and your strategy PDFs: put structured data and document knowledge in the same workspace so answers cite both and nobody has to know where the number lived

An AWS Machine Learning Blog how-to (Nov 3, 2026) shows Amazon Quick combining an enriched sales dataset (text-to-SQL) with an S3 knowledge base of business PDFs inside one Space, then linking a custom chat agent that answers across both with citations. Example questions span “How did sales change by region?” and “How do 2025 results align with our market analysis memo?” The walkthrough covers semantic column descriptions, custom dataset instructions, Spaces, persona instructions, sharing with least-privilege access, and generating PowerPoint/Excel/Word from chat. Needs an Amazon Quick Enterprise subscription for dataset creation; adapt the same pattern on any stack that can join warehouse metrics with a document store behind one agent.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- The material the task is about (pasted text, notes or data)

## Instructions

Do the task below with the user's inputs in place of each [bracket].

1. Pick one spreadsheet or warehouse extract (sales, pipeline, support volume) and three trusted PDFs/memos that explain strategy or definitions.
2. Enrich the table: correct types, write a dataset description, usage rules (currency, fiscal year, how Sales is calculated), and a one-line description per important column.
3. Index the documents in a knowledge base and put the dataset + knowledge base in one shared workspace your chat agent can see.
4. Ask three questions that need both sources (trend from the numbers + “why” from a memo) and require citations on every figure.
5. Share only after access matches who may see those docs; keep a human check before any slide or number leaves the company.

## Safety checks (share these with the user)

- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: Amazon Quick (Spaces, datasets, knowledge bases, chat agents) or similar BI+RAG agent stack, S3 or document store, spreadsheet/warehouse extract
- Based on this real-world example: https://aws.amazon.com/blogs/machine-learning/bring-structured-and-unstructured-data-together-with-amazon-quick/
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-637-one-chat-agent-for-both-your-sales-spreadsheet-and-your-stra/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/