Golgent Lifestyle Discovery

Help users discover lifestyle options that match their intent — from shopping and dining to local services and everyday choices. Zero setup required: no registration or API key needed.

Core use cases

• - Shopping — Buy products, find deals, compare prices across e-commerce platforms
• Dining & food delivery — Order food, discover restaurants, find nearby takeout
• Local services — Find service providers, compare local options
• Travel & activities — Discover nearby activities, weekend plans, travel ideas
• Everyday choices — "What should I choose?" decisions with budget/preference constraints

Workflow

1. 1. Identify the category. Map user intent to a category (see guidance below).
2. Ask only the minimum clarifying questions needed. Don't over-ask — if intent is clear, proceed.
3. Ask for location only when the scenario requires it. food_delivery needs precise location; ecommerce does not.
4. Ask for consent before sending optional profile data. Follow the consent flow in references/privacy.md.
5. Build structured keywords and filters. Extract 1–3 Chinese keywords + price/sort/platform filters.
6. Call the API. POST https://ads-api.usekairos.ai/ads/neo — see references/api.md for full schema.
7. Present results as concise, actionable options. Use the formatting rules below.

Category guidance

User Intent	INLINECODE6	Location
Buy products, shopping, deals	INLINECODE7	Not needed
Order food, restaurants, takeout

food_delivery | Precise address/coordinates required | | General / broad discovery | (omit field) | Depends on context |

API quick reference

Endpoint: INLINECODE9

Minimal request:

CODEBLOCK0

Key fields: category, search_keywords (1–3 Chinese keywords), filters (pricemin, pricemax, sortby, platform, freeshipping, location, latitude, longitude), total_count.

→ Full request/response schema: INLINECODE14

Privacy rules

1. 1. NEVER send phone, email, name, ID, or payment data — even if the user shares them.
2. Ask explicit consent before sending optional user profile fields (keywords, gender, yob, longtermprofile).
3. Location by scene: food_delivery needs precise location; other local services need city name; ecommerce needs nothing.
4. Transparency: Always tell users that results come from external platforms.
5. No third-party sharing: User data is never shared with merchants or platforms.

→ Full privacy policy and consent flow: INLINECODE18

Result formatting

• - Summarize 3–5 best options in a Markdown table.
• Show transparency note: "以下是根据你的需求从多个平台搜索到的推荐："
• Use [cta_text](click_url) links — never paste raw URLs.
• Show strikethrough original price when discount exists.
• If fill_status is "no_fill": "暂时没有找到相关推荐，换个关键词试试？"

→ Formatting templates and examples: INLINECODE22

When NOT to use this skill

• - Pure knowledge questions (e.g. "什么是量子计算")
• Recipe instructions or cooking tutorials
• Information queries with no purchase/recommendation/comparison action
• When there is no reason to ask for the user's location or profile

Read references when needed

Need	File
API fields, request/response schema, error codes, rate limits	INLINECODE23
Privacy policy, consent flow, compliance details

references/privacy.md | | curl / Python / TypeScript examples, formatting templates | references/examples.md | | Scene mapping, keyword extraction rules, sample prompts, listing copy | references/positioning.md |