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-ad-architecturegit 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-ad-architecture)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ad-architecture"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ad-architecture/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-ad-architecture"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ad-architecture.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 | $0.00186 | $0.01464 |
| Opus 5 | $0.00093 | $0.00732 |
| Sonnet 5 | $0.00037 | $0.00293 |
| Haiku 4.5 | $0.00019 | $0.00146 |
Grade A, and why
sealeap-amazon-ad-architecture 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SeaLeap 亚马逊广告架构
围绕阶段销量和退货后利润构建广告组合，而不是按广告类型堆 Campaign。先算经营上限，再选择关键词和各广告活动的职责，最后用周复盘和单变量实验调整。
强制边界
- 把源 PPTX 中的日期、客单价、利润率、退货/仓储占比、搜索量区间、SPR/CPR、PPC、预算比例、订单和 ACOS 标记为
TRAINING_CASE，不得直接套入当前 ASIN。 - 不承诺“投广告即可把关键词推上首页”。自然排名受相关性、转化、销量、竞争和平台机制共同影响，只能把排名变化设为伴随指标。
- 使用当前账户的广告报告、业务报告、库存、退货、价格、费用和 Listing 证据；数据不足时输出
HOLD / NEEDS_DATA，不编数。 - 对不明确的缩写（如材料中的 SPM、SPA、KT、LW、MD、BP/BPE、CT）先建立数据字典，不猜测后执行。
- 广告可用性、归因、竞价策略、placement 和政策以当前站点/控制台为准。
- profile/store/ASIN/SKU 是业务对象，不是授权。生产写入必须绑定服务端已验证 scope，并逐项人工确认。
- 一张实验卡只改一个主变量；不要同时改 Listing、价格、Coupon、预算、竞价、匹配方式和创意后声称因果。
阅读 references/source-and-guardrails.md 获取 PPTX 页码映射、案例口径与限制。
工作流
1. 固定对象和目标
记录 marketplace、profile、ASIN/SKU/父子体、品类、生命周期/季节、价格、销量目标、利润目标、旺季截止、库存、补货周期和负责人。
将目标按阶段拆开，每阶段只保留：日期、目标单位数、最低利润/最大可接受亏损、关键词/人群任务和停止条件。不要只写“提高销量”。
2. 倒推经济上限
用真实费用计算净收入、退货后贡献利润、盈亏平衡 ACOS/CPA 和最大广告花费。广告预算不得超过经营上限、库存上限或季节剩余机会中的最小值。
使用 references/economics-and-stages.md 的公式和三阶段模板。源材料的 $50 季节品案例只用于演示算法。
3. 建关键词/商品机会表
从搜索词、Search Query Performance/Brand Analytics、广告报告和当前可用的第三方研究取得证据。逐个验证相关性、意图、搜索量、CPC、CVR、竞争、自然/广告位置和利润容量。
按核心/中等/长尾/场景/竞品意图分层，不用固定 10 万/1 万搜索量阈值。使用 references/keyword-plan.md。
4. 为每个阶段分配广告职责
按任务选择广告类型：
- SP 自动用于发现，SP 手动用于验证关键词/商品定向；
- SB/SBV 用于品牌入口、视频卖点和额外搜索承接；
- SD 用于当前可用的商品/受众再营销或扩展；
- 只有具备资格、素材、落地页和可归因目标时才分配预算。
用 references/campaign-portfolio.md 设计 Campaign/Ad Group、匹配与预算，不复制案例百分比。
5. 处理红海/高竞争场景
当核心大词的 SP 成本超过利润容量时，不用更高出价掩盖问题。先验证 Listing/价格/评论/库存，再比较 SB/SBV、商品定向、长尾和 SD 是否带来可盈利增量。
阅读 references/red-ocean-playbook.md；材料中“SB 带来 210 单、占广告订单 40%”只属于一个 2023 年讲师案例。
6. 形成可审批架构
输出阶段目标、经济模型、关键词任务、Campaign map、预算、样本门槛、单变量动作卡与回退值。使用 references/output-contract.md。
7. 周复盘与降档
- 同时看广告销售、自然销售、总销售、TACOS、退货后利润、库存和关键词位置；不要只看 ACOS。
- 标记价格、优惠、评论、断货、竞品、季节和归因延迟等干扰项。
- 依据预设样本/日期判断
KEEP、ITERATE、ROLLBACK或STOP。 - 旺季剩余时间短于学习/补货/回收窗口时停止扩量并执行降档。
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 · 81 lines · 186 tokens per session scan A eaee318d5e1f
sealeap-amazon-ad-architecture is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 186 tokens to every session and 1,464 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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