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.
npx skills add xjli360/sealeap-amazon-ad-skills --skill sealeap-amazon-fashion-category-growthgit clone --depth 1 https://github.com/xjli360/sealeap-amazon-ad-skillsWrote 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.
[](https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-fashion-category-growth)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-fashion-category-growth"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-fashion-category-growth/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.
<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-fashion-category-growth"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-fashion-category-growth.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00153 | $0.01049 |
| Opus 5 | $0.00077 | $0.00524 |
| Sonnet 5 | $0.00031 | $0.00210 |
| Haiku 4.5 | $0.00015 | $0.00105 |
Grade A, and why
sealeap-amazon-fashion-category-growth 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 8d 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.
What it actually says
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。
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 62 lines · 153 tokens per session scan A 3ccdd863d88c
sealeap-amazon-fashion-category-growth is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 153 tokens to every session and 1,049 once invoked, about $0.0008 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 2026-09-04.
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