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 | $0.00084 | $0.02378 |
| Sonnet 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 4d 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.
- 4d ago Changed · -1 lines 2b236ed4ef97
- 12d 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 6d ago), licensed MIT. It adds 167 tokens to every session and 4,756 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.
Other skills, from other repositories
zach-seller-skill-creator
A Chinese-language guide for Amazon sellers who want to turn repeated work processes into reusable skills for an AI agent.
zach-search-term-analyzer
An analyzer for Amazon Brand Analytics Top Search Terms reports, which show popular searches across Amazon and how clicks and conversions are distributed among products.
zach-search-term-report-analyzer
An Amazon Ads search-term report analyzer for Sponsored Products, Sponsored Brands, and Sponsored Display campaigns. It groups related search terms, measures results over 7, 14, and 30 days, and produces reports in several file formats.
amazon-market-trend-scanner
Amazon category trend scanner. Scans Amazon category landscapes to discover trending subcategories, emerging niches, and market shifts. Tracks demand surges, brand consolidation, new entrant waves, price band migration, and margin changes across all subcategories under a parent category. Use when user asks about…
amazon-pricing-command-center
Data-driven pricing strategy engine for Amazon sellers. Given one or more ASINs, auto-detects each product's leaf category, analyzes the pricing landscape, and delivers RAISE/HOLD/LOWER signals with profit simulation. Supports single ASIN or batch (multiple ASINs, auto-grouped by category). Uses ZooData API endpoints…
ecom-applicability
Determine whether AI is appropriate for a specific e-commerce task. Use when evaluating if a problem has enough data, the right tools, or acceptable risk for AI automation. Answers 'should I use AI for X?' with boundary-aware reasoning.