Sealeap Amazon Fashion Category Growth logo

Sealeap Amazon Fashion Category Growth

Community
xjli360
sealeap-amazon-fashion-category-growth

Research and plan Amazon fashion-category growth across the US, Europe, and Japan using trend validation, marketplace-specific selection, brand/store/content tools, promotion economics, inventory routing, and return-reduction gates. Use for 时尚品类选品, 美欧日服饰趋势, fashion opportunity scan, 女装男装童装鞋靴箱包珠宝, 品牌旗舰店与帖子, 促销组合, AWD/FBA库存, 尺码退货, or converting the authorized 2025 fashion guide into a current evidence plan. Route lifecycle campaign execution to the apparel-ads Skills; treat all 2025 trends, product lists, tools, and case figures as snapshots until currently verified.

Overview

Publisherxjli360
Repositorysealeap-amazon-skills
Skill namesealeap-amazon-fashion-category-growth
Stars
308
Forks
44
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by xjli360 on GitHub. Read the source before you install it.

Installation

Install the Sealeap Amazon Fashion Category Growth AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/amazon-official/sealeap-amazon-fashion-category-growth
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git /tmp/sealeap-amazon-skills
mkdir -p .claude/skills
cp -r /tmp/sealeap-amazon-skills/amazon-skills/amazon-official/sealeap-amazon-fashion-category-growth .claude/skills/sealeap-amazon-fashion-category-growth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sealeap Amazon Fashion Category Growth in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sealeap Amazon Fashion Category Growth on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sealeap Amazon Fashion Category Growth is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Amazon 时尚品类增长

目标

将时尚手册中的趋势与商品清单变成验证队列,而不是“热卖推荐”:先用当前站点需求和竞争证据筛选,再检查差异化、合规、利润、库存与退货,最后才进入 Listing、品牌、促销和广告实验。

先读 references/source-and-guardrails.md 和 references/source-map.md。做机会筛选与品牌增长读 references/opportunity-and-brand.md;做库存与退货读 references/inventory-and-returns.md。

与其它 Skill 的分工

  • 本 Skill:类目趋势、选品验证、品牌/促销、库存和退货系统;
  • sealeap-amazon-apparel-lifecycle-ads:美国服饰广告生命周期;
  • sealeap-amazon-jp-apparel-ads、...-uk-...、...-ca-...:站点广告打法;
  • sealeap-amazon-localization-marketing:具体字段与素材本地化。

工作流

1. 固定站点与细分类目

记录 marketplace、product type、性别/年龄、服装/鞋/箱包/珠宝、季节、目标价带和上新时间。不能把美国、欧洲和日本趋势混成一个全球需求。

2. 从源候选建立验证队列

手册的 2025 色彩、面料、廓形、场景和推荐品仅标 SOURCE_CANDIDATE。对每个候选补齐当前搜索需求、销量/点击代理、竞争、评价门槛、退货主题、季节窗口和来源日期。

3. 产品可行性闸门

检查事实/材质、尺码体系、版型一致性、色差、标签、目标站点法规、知识产权、供应商能力、MOQ、交期、变体复杂度、落地成本、贡献毛利、退货敏感性和库存风险。任一硬闸门失败即 REJECT。

4. 建立本地商品表达

用当前目标站点搜索词和评价重建标题、属性、尺码表、图片/视频和使用场景。模特信息、颜色/面料特写、包容性和穿搭灵感都必须与实物一致。

5. 规划品牌与内容

只有完成 Brand Registry/当前资格核验后,才考虑品牌店、帖子、品牌受众优惠或 AI 内容工具。选择一个目的:建立信任、帮助搭配、解释面料/版型、交叉销售或复购;不要为了使用工具而创建内容。

6. 计算促销

核验当前 Deal/Coupon 规则、参考价、费用、库存和毛利。爆品集中、折扣深度、本地活动和品类活动的课程结果是历史调查,不是促销配方。

7. 设计库存与退货方案

按旺季/常青、多变体、FBA/AWD/其它当前可用履约方式建情景;用退货原因反推尺码、颜色、材质、图片和质量改进。库存清理动作先算回收价值与品牌影响。

8. 输出一个验证实验

只验证一个核心假设,例如某本地趋势、尺码表达、面料特写、搭配图或促销结构。固定价格/流量等主要变量,设置成功/停止线和回退。

必须交付

  • 站点/细分类目/季节作用域;
  • SOURCE_CANDIDATE → CURRENT_EVIDENCE → DECISION 队列;
  • 合规、利润、供应链、库存和退货闸门;
  • 品牌/内容/促销草案;
  • 一个实验和 GO / HOLD / REJECT 状态。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sealeap Amazon Fashion Category Growth AI skill do?

Research and plan Amazon fashion-category growth across the US, Europe, and Japan using trend validation, marketplace-specific selection, brand/store/content tools, promotion economics, inventory routing, and return-reduction gates. Use for 时尚品类选品, 美欧日服饰趋势, fashion opportunity scan, 女装男装童装鞋靴箱包珠宝, 品牌旗舰店与帖子, 促销组合, AWD/FBA库存, 尺码退货, or converting the authorized 2025 fashion guide into a current evidence plan. Route lifecycle campaign execution to the apparel-ads Skills; treat all 2025 trends, product lists, tools, and case figures as snapshots until currently verified.

Why use Sealeap Amazon Fashion Category Growth on TypingMind?

Because you install it once and use it with any model. Sealeap Amazon Fashion Category Growth is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sealeap Amazon Fashion Category Growth in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/amazon-official/sealeap-amazon-fashion-category-growth. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sealeap Amazon Fashion Category Growth?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sealeap Amazon Fashion Category Growth?

As many as you like. As long as a model supports skills, you can use Sealeap Amazon Fashion Category Growth with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sealeap Amazon Fashion Category Growth AI skill free?

Yes. It is published on GitHub by xjli360 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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