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-apparel-lifecycle-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-apparel-lifecycle-ads)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-apparel-lifecycle-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-apparel-lifecycle-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-apparel-lifecycle-ads"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-apparel-lifecycle-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 | $0.00167 | $0.04756 |
| Opus 5.5 | $0.00067 | $0.01902 |
| Sonnet 5.5 | $0.00033 | $0.00951 |
| Haiku 4.5 | $0.00017 | $0.00476 |
Grade A, and why
sealeap-amazon-apparel-lifecycle-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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
亚马逊美国站服饰品类 · 生命周期广告投放法则
来源：亚马逊广告官方系列直播课《ASIN 决金局 · 亚马逊美国站服饰广告系列课》的长生命周期（两个视频版本）、短生命周期与季节性内容（本地共 4 个视频文件，2026 年 4 月） 倍数型数据通常为「头部 25% ASIN ÷ 尾部 25% ASIN」的相对值，非绝对值；Costume Outfit 的源画面存在头尾 20% / 25% 分位冲突，未复核前不得用作账户目标。
先读 references/source-and-guardrails.md 理解课程主题、公开范围、证据等级与执行边界。课程中的广告产品能力、资格和规则是 2026 年课程时点信息；涉及真实执行时必须核对当前美国站控制台。
0. 这个 skill 解决什么
服饰是非标品：同一个关键词背后可能对应完全不同的消费者画像，流量更容易泛化。 课程认为，照搬标品打法（先铺满入口 → 再筛选 → 加价上首页）在服饰品类容易失焦；是否失效必须用具体账户数据验证。
本 skill 提供的决策链条：
判断周期类型 → 判断细分产品类型 → 确定广告锚点 → 判断当前所处阶段
→ 套用该阶段的广告架构与预算配比 → 按触发条件调优 → 运营动作补位
使用顺序：先做第 1-4 步的诊断，再进入第 5 步的打法章节。不要跳过诊断直接给架构建议。
1. 第一步：判断产品属于哪个生命周期类型
1.1 三个阶段的课程基础定义
以下月龄定义用于长、短生命周期产品的初始判断；季节性产品必须按细分类型改用旺季窗口：纯季节型为“第一个旺季前 / 第一个旺季 / 第二个旺季及以后”，周期型课程案例为“第一轮促销周期 / 第二轮 / 第三轮及以后”，见 references/seasonal.md。
| 阶段 | 定义 | 特征 |
|---|---|---|
| 新品期 | 上架开售当天起 0–3 个月 | 缺曝光、缺流量、缺市场认知，评论少 |
| 成长期 | 上架第 3 个月 → 销售额达到历史峰值 | 销售快速上升，评论/流量由少变多 |
| 成熟期 | 从销售峰值 → 跌至峰值 50% 以下之前 | 销售自峰值回落但有反复，自然流量开始进来 |
| 衰退期 | 跌破峰值 50% 之后 | 非广告投放的关键阶段 |
1.2 三大周期类型的判定
| 类型 | 趋势图特征 | 销售期 | 典型品类 |
|---|---|---|---|
| 长生命周期 | 稳定爬坡后长期高位，36 个月后仍在峰值 50% 以上 | > 3 年 | 西装 Suit、连衣裙（基础款）、紧身衣、打底裤/塑身衣、太阳镜、领带、配饰 |
| 短生命周期 | 快速上升 + 快速下跌，窗口极短 | < 3 年 | 文胸 Bra、内裤 Underpants、睡衣睡裙、浴袍、束腰带、身体束带、腰带、钥匙扣 Keychain |
| 季节性 | 高峰低谷规律可预测，旺季窗口集中 2–4 个月 | 多轮周期 | 泳装 Swimwear、女裙 Dress、外套、手套、毛衣、靴子、角色扮演服 Costume Outfit |
1.3 ⚠️ 关键补充：日历时间是明线，“标签语义”是课程诊断模型
不能只按上架天数判断阶段。 课程用“标签建立/强化/稳定/漂移”解释 ASIN 流量语义的变化，但它不是 Amazon 后台可直接读取的状态字段。使用时标记为 COURSE_MODEL，并从搜索词相关性、类目/商品定向流量、点击与转化、自然词覆盖和买家行为中推断：
| 课程模型 | 运营含义 | 大致对应 |
|---|---|---|
| 标签建立期 | 相关流量与转化语义仍在形成 | 新品期 |
| 标签强化期 | 已出现可重复的相关流量与成交信号 | 成长期 |
| 标签稳定期 | 主流量池和成交属性较稳定 | 成熟期 |
| 标签漂移期 | 流量、页面承诺与成交人群可能出现偏移 | 需先诊断再决定是否放量 |
课程中的判断示例（不是平台硬阈值）：
- 上架约 3 个月但相关流量仍分散、主场景未形成 → 可按“实质仍在新品期”诊断；先收敛定位与流量，再小步验证放量。
- 上架约 1–2 周已出现清晰主场景和可重复成交 → 可能开始进入成长期；仍需满足利润、库存和样本护栏后再放大。
2. 第二步：判断细分产品类型与广告锚点
八个课程案例/细分象限，锚点决定后续动作：
What ships with it
10 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.
- agents/openai.yaml 304 B
- references/benchmark-data.md 9.4 KB
- references/faq.md 8.6 KB
- references/keyword-research.md 7.8 KB
- references/long-lifecycle.md 14 KB
- references/operations.md 8.0 KB
- references/output-contract.md 4.7 KB
- references/seasonal.md 17 KB
- references/short-lifecycle.md 17 KB
- references/source-and-guardrails.md 4.8 KB
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 Changed · -1 lines 2b236ed4ef97
- 1mo ago First seen · 213 lines · 167 tokens per session scan A 88c3b057acab
sealeap-amazon-apparel-lifecycle-ads is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 167 tokens to every session and 4,756 once invoked, about $0.0007 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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