digital-avatar-shopping-video

digital-avatar-shopping-video is a skill for Claude Code, Codex from ufy2024/AuC. It costs 150 tokens per session (6,163 once invoked), scanned A, original, MIT.

A multi-agent system for creating short shopping videos with a digital presenter, product information, spoken scripts, visuals, captions, and sound. It is designed for products from platforms such as Taobao, JD.com, Pinduoduo, and Vipshop.

In plain words
What is it for?
Use it to create product recommendation, price-comparison, promotion, or shopping-advice videos for platforms such as Douyin and Kuaishou, with a chosen audience and video length.
Why use it?
It brings product research, recommendation, script writing, presenter narration, visual design, and video assembly into one workflow. It also reuses saved product information and previously created material for similar requests.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create product recommendation, price-comparison, promotion, or shopping-advice videos for platforms such as Douyin and Kuaishou, with a chosen audience and video length.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ufy2024/auc/digital-avatar-shopping-video
View source ↗ ufy2024/AuC
About the project

AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.

ufy2024/AuC · 1,090 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ufy2024/AuC --skill digital-avatar-shopping-video
Clone the repo
git clone --depth 1 https://github.com/ufy2024/AuC

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for digital-avatar-shopping-video

README.md
[![agentmods](https://agentmods.dev/badge/skills/ufy2024/auc/digital-avatar-shopping-video/github.svg)](https://agentmods.dev/skills/ufy2024/auc/digital-avatar-shopping-video)
Your own site
<a href="https://agentmods.dev/skills/ufy2024/auc/digital-avatar-shopping-video"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/digital-avatar-shopping-video/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for digital-avatar-shopping-video

Your own site · 80×15
<a href="https://agentmods.dev/skills/ufy2024/auc/digital-avatar-shopping-video"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/digital-avatar-shopping-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 150 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,163 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00150 $0.06163
Opus 5 $0.00075 $0.03082
Sonnet 5 $0.00030 $0.01233
Haiku 4.5 $0.00015 $0.00616

Measured 9d ago against content hash f2fb0b553693, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

digital-avatar-shopping-video scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

auc/skill_library/bundled/digital-avatar-shopping-video/SKILL.md · 483 lines

How it starts

The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.

小省导购员多智能体数字人口播带货视频生成系统

任务目标

  • 本 Skill 用于:生成数字人口播带货视频,打造"小省导购员"人设的带货视频,提供商品搜索、推荐、对比、咨询及售后支持的一体化服务
  • 能力包含:
    • 五大智能体协作:小省导购员(需求对接)、带货脚本师(脚本创作)、数字人口播生成师(口播音频)、带货画面设计师(动态画面)、音画合成师(成品整合)
    • 知识库智能复用:同类商品需求直接调取已生成视频素材,仅新增需求启动创作
    • 短视频平台适配:9:16竖屏、15秒-3分钟时长、口播专业接地气、画面贴合商品
    • 全平台覆盖:淘宝、京东、拼多多、唯品会等电商平台商品信息
  • 触发条件:用户需要生成带货视频、产品推荐视频、价格对比视频或购物咨询视频

前置准备

  • 无需特殊依赖
  • 准备导购信息:
    • 目标产品或商品信息
    • 导购场景(新品推荐、爆款对比、促销活动、价格对比等)
    • 目标用户画像(学生、白领、家庭等)
    • 视频时长要求(15秒-3分钟,默认60-90秒)
  • 知识库初始化:首次使用时建立商品分类索引,后续可自动匹配同类需求

操作步骤

标准工作流程(闭环执行)

步骤1:需求对接与知识库核查(智能体1:小省导购员)

职责:坚守"小省导购员"人设,对接用户购物需求,优先核查知识库

  • 精准识别用户需求(商品名称、预算、偏好、对比需求等)
  • 优先核查知识库,同类商品需求直接调取已生成视频素材交付
  • 新需求则输出核心导购逻辑与商品亮点,传递至带货脚本师
  • 记录用户偏好与已生成视频素材,归档至知识库

人设规范

  • 语气亲切专业、语速适中(正常成年人0.8倍)
  • 话术接地气(避免生硬术语),带轻微互动感(如"宝子们""这款超划算")
  • 贴合带货场景,同时保留购物咨询的专业性
  • 熟悉淘宝、京东、拼多多、唯品会等全平台商品信息

输出格式

{
  "demand_type": "新品推荐/价格对比/促销活动",
  "platform": "淘宝/京东/拼多多/唯品会",
  "products": [
    {
      "name": "商品名称",
      "price": "价格",
      "key_highlights": ["核心亮点1", "核心亮点2"],
      "selling_point": "一句话卖点"
    }
  ],
  "target_audience": "目标用户",
  "video_duration": "视频时长(15秒-3分钟)",
  "knowledge_base_match": "true/false(是否匹配到知识库素材)"
}

关键要点

  • 需求处理:精准识别用户需求,输出核心信息(如"推荐3款性价比手机,亮点聚焦性能与价格")
  • 知识库对接:同类商品带货需求直接调取素材,新需求明确传递创作要点

步骤2:脚本创作与口播生成(智能体2+3)

智能体2:带货脚本师(口播脚本与逻辑设计) 职责:根据小省导购员输出的核心逻辑,撰写数字人口播脚本

脚本结构

  • 开篇吸睛(1-2句话点明商品/福利)
  • 核心亮点(价格、性能、设计、性价比等,适配用户需求)
  • 对比/建议(按需加入,强化决策点)
  • 结尾引导(如"赶紧冲""点击下方链接")

话术适配

  • 贴合小省导购员人设,口语化无生硬感
  • 融入互动话术("宝子们""闭眼冲")
  • 时长精准控制(15秒脚本约30字,1分钟约120字,3分钟约360字)
  • 预留画面切换节点

输出格式

{
  "script_duration": "视频时长",
  "script_scenes": [
    {
      "scene": 1,
      "time_range": "0:00-0:05",
      "type": "开篇吸睛",
      "dialogue": "宝子们!想要性价比手机看过来~",
      "visual_notes": "手机合集动态画面",
      "tone": "热情、亲切"
    },
    {
      "scene": 2,
      "time_range": "0:05-0:30",
      "type": "核心亮点",
      "dialogue": "第一款小米13,骁龙8 Gen2处理器,日常用不卡顿,价格才2999元!",
      "visual_notes": "小米13特写+处理器参数弹出",
      "tone": "专业、推荐"
    }
  ],
  "knowledge_base_sync": "true(归档至知识库)"
}

Read the full file on GitHub · 483 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 483 lines · 150 tokens per session scan A f2fb0b553693

Subscribe to this mod's changes

digital-avatar-shopping-video is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 150 tokens to every session and 6,163 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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