Grounding Agent Answers in Verified Product FAQs

Where this fits

A customer asks your support agent whether the S2 Pro supports 5GHz Wi-Fi. Without a product knowledge base, the agent would likely guess—and risk giving a wrong spec. product-faq gives the agent a strict retrieval-and-cite workflow for product parameters, pricing, warranties, returns, logistics, and membership points.

Why agents benefit

• Retrieves answers only from your knowledge base via kb_search, reducing hallucinated specs.
• Forces source citation (e.g., "依据: FAQ-001") on every answer, making responses auditable.
• Detects ambiguity: if multiple product versions match, the agent asks a clarifying question instead of assuming.
• Handles structured output: short numeric lists for parameters, tables for comparisons, direct conclusions first.
• Fails safely: when no document is found, it says "I need to verify" and suggests human handoff instead of inventing.

Practical scenarios

Spec comparison during shopping

A customer asks "What's the difference between X1 and X1 Pro?" The agent pulls related FAQ entries and renders a comparison table with brightness, battery, and waterproofing, each row cited.

Return policy before purchase

A customer wants to know whether opened headphones can be returned within 14 days. The skill checks the FAQ and returns the exact policy, including restocking fee and required packaging.

Warranty claim routing

A customer reports a malfunctioning drone. The product-faq skill recognizes this as a product question, retrieves warranty terms, then hands off to complaint-handling with the warranty context.

Add it to your agent workflow

Install via:

npx skills add zj-unicom-ai/UniEmployee --skill product-faq

Then register the skill with your agent orchestration layer. In a typical turn, the skill's execution steps look like:

1. Confirm product model if ambiguous
2. kb_search keywords from user query
3. Return cited answer; if no result, suggest human handoff

The skill maps directly to your agent's intent router: when the user's query mentions product specs or policies, trigger product-faq before falling back to generic chat.

Compare before adopting

Before you wire this into production, compare it with alternatives on four fronts:

• Quality signals: Check the README and recent commits—do they maintain the FAQ schema and citation format?
• Maintenance freshness: Look for updates to the product line and whether the skill's routing rules match your catalog.
• Alternatives: You could build an internal intent + retrieval flow yourself; weigh the effort against using this standardized one.
• Workflow fit: The skill assumes kb_search and a document entry format. Make sure your knowledge base has comparable FAQ entries or adapt the skill's retrieval abstraction.

For a more detailed breakdown, see the skill page at https://www.openagentskill.com/skills/zj-unicom-ai-product-faq.

Why it is worth tracking

The skill has 21 stars and comes from zj-unicom-ai's UniEmployee repo, so it is early but demonstrates a clear production pattern: grounded retrieval, explicit no-answer behavior, and structured presentation. Watch how often the underlying FAQ data gets refreshed and whether the author adds integrations for other knowledge sources. If you operate a customer-facing agent that answers product questions, this is a lightweight, auditable pattern worth evaluating now, before you build your own from scratch.