growthylab/golgent-lifestyle-discovery

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. 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 | category | Location | |-------------|-----------|----------| | Buy products, shopping, deals | ecommerce | Not needed | | Order food, restaurants, takeout | food_delivery | Precise address/coordinates required | | General / broad discovery | (omit field) | Depends on context |

API quick reference

Endpoint: POST https://ads-api.usekairos.ai/ads/neo

Minimal request:

{
  "category": "ecommerce",
  "search_keywords": ["降噪耳机"],
  "total_count": 3
}

Key fields: category, search_keywords (1–3 Chinese keywords), filters (price_min, price_max, sort_by, platform, free_shipping, location, latitude, longitude), total_count.

→ Full request/response schema: references/api.md

Privacy rules

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, long_term_profile).
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: references/privacy.md

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: references/examples.md

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 | references/api.md | | 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 |