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-jp-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-jp-apparel-ads)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-jp-apparel-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-jp-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-jp-apparel-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-jp-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 | $0.00169 | $0.01779 |
| Opus 5 | $0.00084 | $0.00890 |
| Sonnet 5 | $0.00034 | $0.00356 |
| Haiku 4.5 | $0.00017 | $0.00178 |
Grade A, and why
sealeap-amazon-jp-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 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SeaLeap 亚马逊日本站服饰广告运营
把日本站服饰 ASIN 的产品类型、生命周期阶段、季节节点、消费者意图和账户真实数据合并成可审核的广告计划。先诊断，再选择日本站打法；不要把美国站或英国站的节奏直接套入日本站。
强制边界
- 只将本 Skill 用于 Amazon.co.jp 服饰及与服饰紧密相关的配件。跨站点需求必须重新验证关键词、季节和消费者意图。
- 把手册中的百分比、倍数、预算结构和竞价建议标记为
MANUAL_BASELINE，不当作当前账户的事实或必达 KPI。 - 优先使用当前账户一方数据：Campaign/Search Term/Targeting/Placement 报告、广告销售、总销售、利润、库存、退货和 Listing 证据。缺数据时输出
DRAFT / HOLD，不编数。 - 把
store_id、seller ID、profile ID 或 ASIN 当作业务对象，不当作授权。任何真实读写都要绑定服务端已验证的店铺与广告 profile scope。 - 默认只产出草案。调整预算、竞价、placement、target、否定词、状态或广告结构前，逐项展示旧值、新值、证据、影响范围和回退值，并等待人工确认。
- 每张实验卡只改一个主变量。不要同时改 Listing、价格、优惠、库存、竞价和定向后声称因果。
查看 references/source-and-guardrails.md 获取原 PDF/新增文章映射、数据口径和不可直接执行的阈值说明。
工作流
1. 锁定投放对象
取得并固定：
- 广告 profile、seller、marketplace=
JP、ASIN、SKU、父子体和产品类型； - 上架日、当前销售曲线、历史峰值、季节节点、在途/可售库存和补货周期；
- 价格、优惠、Amazon Points 当前设置、星级、评论量、退货原因和单件贡献利润；
- 近30/14/7天广告数据，至少按 query/target、match type、placement、ASIN/SKU 拆分；
- 当前 Listing 的主图、标题、五点、尺码表、A+、日文本地化和功能性声明证据。
信息不完时明确列出 NEEDS_DATA，继续做有边界的诊断，不用想象补齐。
2. 判定生命周期类型与阶段
先根据真实销售曲线判定，再用手册案例类比：
| 类型 | 判断信号 | 日本站手册案例 | 主要任务 |
|---|---|---|---|
| 长生命周期 | 全年需求相对稳定，靠评论与品牌长期累积 | 背包 | 新品建信任，成长期放大规模，成熟期守阵地与再营销 |
| 短生命周期 | 快速起量后进入不可逆衰退，产品迭代快 | 内裤 | 新品用功能精准词起量，成长期建品牌，成熟期激活复购并让新款接力 |
| 季节性 | 需求集中在明确月份，错过窗口难以补救 | 泳装 | 旺季前抢排名和视觉信任，爬坡期放量，高峰期收割并累积品牌 |
阅读 references/market-and-lifecycle.md 判定日本站消费者特征、全年节奏和生命周期边界。不要仅按上架月数硬套阶段；销售趋势、需求节点和库存风险必须一起判断。
3. 做五层诊断
按“现状 → 证据 → 问题 → 动作 → 验证指标”输出：
- 可售性：库存、Buy Box、抑制、价格、配送、资格和季节备货是否支持放量。
- 可发现性：日文品类词、功能词、场景词和商品定向是否匹配真实产品。
- 点击：搜索结果中主图、标题前段、价格/积分、评分和视频首帧是否建立当地化信任。
- 转化与退货：尺码、材质、功能、做工、使用场景和限制是否在 Listing 中被如实说清。
- 利润与增量：把 CPC、CVR、ACOS、TACOS、广告/自然/总订单、退货后贡献利润和库存消耗合并判断，不以单一 ROAS 下结论。
4. 路由到站点专属打法
- 长生命周期或高信任门槛产品：阅读 references/long-lifecycle.md。
- 短生命周期、高频消耗或款式迭代产品：阅读 references/short-lifecycle.md。
- 需求高度集中在旺季的产品：阅读 references/seasonal-lifecycle.md。
What ships with it
7 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 Changed 04b9af19e30f
- 12d ago First seen · 96 lines · 169 tokens per session scan A 8c9df4c2099a
sealeap-amazon-jp-apparel-ads is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 169 tokens to every session and 1,779 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 2026-08-30.
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ecom-compliance
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