Product Recommender

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name: product-recommender description: > Intelligent product recommendation engine for retail digital employees. Recommends products based on customer needs, budget, recipient, occasion, preferences, and purchase history. Supports gift recommendations, outfit pairing, cross-sell, upsell, and "help me decide" flows. Use when someone asks: 推荐, 适合, 送礼, 哪个好, 帮我选, 比较一下, 搭配什么, 有什么好的, recommend, what should I get, best for, gift idea, help me pick, what goes with, suitable for, I'm looking for. metadata: openclaw: emoji: 🎯

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Product Recommender

Overview

This skill handles all "help me choose" queries. It goes beyond listing products — it understands the customer's situation, filters intelligently, and presents a curated shortlist with reasons.

Depends on: products[] in knowledge base (Step 03). Works better with: inventory data (to exclude out-of-stock items).

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Intent Extraction

Before recommending, extract these signals from the conversation:

| Signal | Examples | How to Extract | |--------|---------|---------------| | Budget | "500以内", "¥200左右", "不超过1000" | Parse number + direction | | Recipient | "送妈妈", "给男朋友", "自用" | Named or implied | | Occasion | "生日", "面试", "日常穿", "夏天用" | Event or context | | Preferences | "素色", "轻便", "不要太甜", "简约风" | Style/attribute keywords | | Age/Gender | "30岁女性", "老年人", "男生" | Demographic | | Constraints | "不含酒精", "纯棉", "防水" | Hard requirements | | Quantity | "买一套", "各来一个" | Number intent |

If critical signals are missing (especially budget), ask one clarifying question. Never ask for all missing fields at once.

Reference: intent-extraction.md

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Filtering Logic

Apply filters in this order (hard → soft):

1. Hard filters (eliminate if not met): - Budget: price ≤ budget_max (or sale_price if active) - Hard constraints: attribute must match (e.g., "纯棉" → filter by material tag) - Stock: exclude if stock_qty == 0 (when inventory data available)

2. Soft scoring (rank what remains): - Recipient match: suitable_for overlap with recipient description - Occasion match: tags overlap with occasion keywords - Style/preference match: description + tags keyword overlap - Popularity signal: use sales_rank if available, else recency

3. Return top N (default: 3, configurable via max_recommendations)

Reference: filtering-logic.md

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Recommendation Presentation

Standard format (3 recommendations)

为您推荐 3 款最适合的选择：
1️⃣ [产品名] ¥[price]
   [1句话说明为什么适合这个场景/人群]
   [关键亮点：1-2个最相关的属性]
2️⃣ [产品名] ¥[price]
   [...]
3️⃣ [产品名] ¥[price]
   [...]
[可选] 您更倾向哪款？我可以帮您查一下库存～

Gift recommendation (add wrapping note)

送礼推荐：[产品名] ¥[price]
[为什么适合作为礼物 — 1句话]
[礼盒包装是否可用 if known]

Upsell (when appropriate)

If the customer's budget allows 20% more for a meaningfully better option: > "还有一款 ¥[price+] 的[产品名]，多了[key upgrade]，性价比也很高，要不要看看？" Only suggest once per conversation. Never push if customer declines.

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Special Flows

"帮我比较" (Comparison)

When customer names 2+ specific products:

Fetch both from KB

Build a comparison table: price / key specs / suitable for / verdict

Give a clear recommendation, not just data

"搭配什么" (Outfit/Pairing)

When customer asks what goes with a product:

Identify the anchor product

Filter KB for complementary items (matching category tags: "搭配", "配套")

Present as a complete set with total price

"再便宜一点" (Price objection)

When customer asks for cheaper options after seeing recommendations:

Re-filter with lower budget

If nothing cheaper: explain value at current price, don't apologize for price

"没有我想要的" (No match)

When no product passes the filters: 1. Tell the customer honestly 2. Suggest the closest available option 3. Offer to notify when matching product arrives (log as feature request)

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Script

Use scripts/recommend.py for deterministic filtering and scoring. Reference: filtering-logic.md