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-ca-apparel-adsgit 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-ca-apparel-ads)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ca-apparel-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ca-apparel-ads/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-ca-apparel-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ca-apparel-ads.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 | $0.00158 | $0.02454 |
| Opus 5.5 | $0.00063 | $0.00982 |
| Sonnet 5.5 | $0.00032 | $0.00491 |
| Haiku 4.5 | $0.00016 | $0.00245 |
Grade A, and why
sealeap-amazon-ca-apparel-ads 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
亚马逊加拿大站服饰广告 · 生命周期与季节节奏
目标
把加拿大站服饰广告从“照搬美国站”改成一条可复核的决策链：先同时判断 ASIN 生命周期与当年季节窗口，再核对消费者顾虑、英法语流量、利润和库存，最后生成分阶段广告结构与单变量实验草案。
本 Skill 的课程证据只详细覆盖两类产品：
Coat：季节性长生命周期，课程核心是提前布局、旺季加码、旺季后释放积累；Underpants：长生命周期，课程核心是先建立合身/舒适/材质信任，再用品牌与再营销形成规模复利。
先读 references/source-and-guardrails.md。需要预算表和课程对比数据时读 references/lifecycle-playbooks.md；需要消费者与英法语流量背景时读 references/canada-market-and-consumer.md。
不可妥协的边界
- 把课程比例、头尾 ASIN 倍数和消费者研究标为
COURSE_BASELINE；把当前 Campaign、Search Term、Placement、销量、利润、库存、评价与 Listing 数据标为ACCOUNT_ACTUAL。 - 课程中的倍数是加拿大站服饰类目头部 25% ASIN 相对尾部 25% ASIN 的描述性对比，不是目标值、因果证明或执行阈值。
- 不把“更早投入”自动等同于“必然获得排名、复购或更高 ROAS”。先验证产品、可售性、相关性、转化和利润是否支撑。
- 不把法语查询示例直接扩写或翻译后投放。先验证当前 Amazon.ca 搜索相关性、商品事实、页面语言和账户数据。
- 不用无证据的“抑菌、保暖温度、透气提升、舒适度提升”等声明制作广告或 Listing。
store_id、profile ID、ASIN 或 marketplace 只是业务标识，不是授权。所有读取和写入都必须绑定当前验证过的服务端账户范围。- 默认只生成草案。预算、竞价、placement、target、否定词、状态和广告结构均属于外部写操作，必须逐项人工确认。
- 每轮实验只改变一个可归因主变量；不要同时改 Listing、价格、优惠、竞价、预算和定向后声称因果。
先声明模式
在结果顶部选择一种模式：
DIAGNOSE：只读诊断，不生成可执行变更；DRAFT：生成结构、预算重心和实验草案；默认；RELEASE_PREP：生成逐对象新旧值、护栏、回退和审批卡；APPROVED_WRITE：仅执行用户本轮明确批准的对象和单一动作，写后复读。
核心工作流
1. 锁定对象、目标和双时间轴
记录：
- 已验证的 Amazon.ca 广告 profile、seller、ASIN/SKU、父子体、品类和品牌资格；
- 上架日、首次销售日、历史销售峰值、近 36 个月销售曲线和当前生命周期阶段；
- 当前月份、目标旺季起止、距第一波需求的周数、补货周期和库存覆盖；
- 主目标只能选一个：
验证相关性 / 抢旺季流量 / 建立品牌认知 / 守位 / 利润 / 复购； - 贡献毛利率、盈亏平衡 ACOS、目标 TACOS、预算上限和停止条件。
必须分别输出：
ASIN_LIFECYCLE = NEW | GROWTH | MATURE | DECLINE | NEEDS_DATA
SEASON_WINDOW = OFF_SEASON | PREHEAT | PEAK | POST_PEAK | NON_SEASONAL | NEEDS_DATA
Coat 老品每年仍会重新进入 PREHEAT → PEAK → POST_PEAK。生命周期成熟不等于全年只降价；两轴冲突时，以当前需求、库存、利润和账户数据决定动作。
2. 判断课程适配范围
用真实销售曲线判断：
| 轨迹 | 课程代表 | 本 Skill 的处理 |
|---|---|---|
| 季节性长生命周期 | Coat | 使用完整分阶段打法 |
| 长生命周期 | Underpants | 使用完整分阶段打法 |
| 季节性短生命周期 | Shirt | 仅可识别，不得套用 Coat 预算 |
| 短生命周期 | Backpack / Swimwear | 仅可识别，不得套用 Underpants 预算 |
What ships with it
6 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.
- 1mo ago First seen · 169 lines · 158 tokens per session scan A 60022ba90974
sealeap-amazon-ca-apparel-ads is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 158 tokens to every session and 2,454 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-08-30.
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