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-uk-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-uk-apparel-ads)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-uk-apparel-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-uk-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-uk-apparel-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-uk-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 | $0.00176 | $0.01970 |
| Opus 5 | $0.00088 | $0.00985 |
| Sonnet 5 | $0.00035 | $0.00394 |
| Haiku 4.5 | $0.00018 | $0.00197 |
Grade A, and why
sealeap-amazon-uk-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 12d 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 亚马逊英国站服饰广告运营
把英国站服饰 ASIN 的产品类型、生命周期阶段、本地节日、尺码/退货风险和账户真实数据合并成可审核的广告计划。先判断它是长生命周期、短生命周期还是季节性，再按英国站节奏与当前账户证据定义动作。
强制边界
- 只将本 Skill 用于 Amazon.co.uk 服饰及与服饰紧密相关的配件。不要把美国站或欧盟站点的尺码、节庆或竞价直接套入。
- 把手册中的市场规模、搜索量、头尾倍数、预算比例、竞价倍数和固定阈值标记为
MANUAL_BASELINE，不当作当前账户事实或必达 KPI。 - 将 VAT、UKCA、塑料包装税、促销文案、广告资格和品牌徽章视为时效性高的合规问题。执行前核对当前官方规则；本 Skill 不提供法律结论。
- 优先使用当前账户一方数据：Campaign/Search Term/Targeting/Placement 报告、广告/总销售、利润、库存、退货和 Listing 证据。数据不足时输出
DRAFT / HOLD，不编数。 - 把
store_id、seller ID、profile ID 和 ASIN 当作业务对象，不当作授权。任何读写都要绑定服务端已验证的店铺和 Ads 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。
阅读 references/source-and-guardrails.md 获取原 PDF 页码映射、数据口径和高风险阈值说明。
工作流
1. 锁定投放对象
取得并固定：
- 广告 profile、seller、marketplace=
UK、ASIN、SKU、父子体和产品类型； - 上架日、当前销售曲线、历史峰值、旺季节点、在途/可售库存、补货周期与英国天气风险；
- 价格、优惠、星级、评论量、退货原因、VAT/产品合规状态和单件贡献利润；
- 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、UK/EU 尺码表、材质、长度/版型、A+、视频和英国英语本地化。
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
| 类型 | 判断信号 | 英国站手册案例 | 主要任务 |
|---|---|---|---|
| 长生命周期 | 全年需求稳定，评论和品牌可持续积累 | 女士睡衣 | 新品建面料/版型信任，成熟期扩大规模、防守品牌与激活复购 |
| 短生命周期 | 单峰快速上升后持续衰退，下一季不一定恢复 | 泳衣 | 低成本建关键词历史，旺季放量，高峰后按日历收缩并让新款接力 |
| 季节性 | 每年周期性回升，多年峰值可复利 | 外套 | 旺季前预热，黑五与 Boxing Day 连续放量，淡季防守并唤醒老客 |
阅读 references/market-and-lifecycle.md 判定英国站消费者特征、Q4 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、库存、退货和旺季日历必须一起判断。
3. 做六层诊断
按“现状 → 证据 → 问题 → 动作 → 验证指标”输出：
- 合规与可售性：VAT/适用认证、库存、Buy Box、抑制、价格、促销、配送和资格是否支持放量。
- 可发现性：英国英语品类词、功能词、礼品/节日词和商品定向是否匹配真实产品。
- 点击：主图、标题前段、价格、评分、视频首帧和本地场景是否建立信任。
- 转化与退货：UK/EU 尺码、长度、胸围、版型、材质、透明度、保暖/防水声明和色差是否被如实说清。
- 利润与增量：合并 CPC、CVR、ACOS、TACOS、广告/自然/总订单、退货后利润和库存消耗，不以单一 ROAS 下结论。
- 季节可执行性：创意审核、入仓、优惠、Black Friday、Boxing Day、天气变化和预算降档是否有明确日历。
4. 路由到站点专属打法
- 长生命周期或睡衣/内衣类高信任门槛品：阅读 references/long-lifecycle.md。
- 短生命周期、泳衣或款式迭代快的季节品：阅读 references/short-lifecycle.md。
- 外套等每年可回升的季节性产品：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.
- 12d ago First seen · 96 lines · 176 tokens per session scan A 98214741f4f5
sealeap-amazon-uk-apparel-ads is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 5d ago), licensed MIT. It adds 176 tokens to every session and 1,970 once invoked, about $0.0009 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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