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golgent-lifestyle-discovery发现黄金生活方式

Agent Skill

golgent-lifestyle-discovery 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

6,060

周安装

250

GitHub Stars

5

下载量

1,980
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:golgent-lifestyle-discovery(发现黄金生活方式)
来源仓库:https://github.com/uoryon/golgent-lifestyle-discovery
安装命令:
openclaw skills install golgent-lifestyle-discovery
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install golgent-lifestyle-discovery

简介

帮助用户发现购物、餐饮、旅行和日常决策的生活方式选择。

  • 适合需要个性化推荐和本地服务发现的场景。golgent-lifestyle-discovery 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 通过 clawhub 安装,适用于 OpenClaw 中的生活助手需求。
  • 安装前需确认权限范围和维护状态,注意可能的地理位置调用。
  • 建议结合用户偏好和历史记录优化推荐结果。

SKILL.md

name
golgent-lifestyle-discovery
description
Help AI agents discover relevant lifestyle options for users across shopping, dining, local services, travel, and everyday decisions. Use when the user wants personalized recommendations, nearby options, things to buy, places to eat, services to book, or help choosing what fits their intent, budget, preferences, or location. Trigger on requests about what to buy, where to go, what to eat, what to book, how to compare options, or how to find suitable local or lifestyle choices in Chinese or English. 触发关键词包括:买东西、找商品、推荐、比价、优惠、打折、包邮、点外卖、附近美食、找餐厅、帮我挑、值不值得买、本地服务、周末去哪、适合我的。

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 IntentcategoryLocation
Buy products, shopping, dealsecommerceNot needed
Order food, restaurants, takeoutfood_deliveryPrecise 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

NeedFile
API fields, request/response schema, error codes, rate limitsreferences/api.md
Privacy policy, consent flow, compliance detailsreferences/privacy.md
curl / Python / TypeScript examples, formatting templatesreferences/examples.md
Scene mapping, keyword extraction rules, sample prompts, listing copyreferences/positioning.md

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

95.7%
按下载量换算1,895

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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