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-home-lifestyle-category-growthgit 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-home-lifestyle-category-growth)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-home-lifestyle-category-growth"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-home-lifestyle-category-growth/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-home-lifestyle-category-growth"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-home-lifestyle-category-growth.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 | $0.00162 | $0.00931 |
| Opus 5.5 | $0.00065 | $0.00372 |
| Sonnet 5.5 | $0.00032 | $0.00186 |
| Haiku 4.5 | $0.00016 | $0.00093 |
Grade A, and why
sealeap-amazon-home-lifestyle-category-growth 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.
What it actually says
Amazon 生活百货品类增长
目标
把横跨九大生活百货赛道的趋势与候选压缩成逐产品决策：先确定真实使用场景和站点需求，再通过安全/适配/合规、体积物流、利润、售后和库存门槛，最后才选择品牌工具、促销和广告。
先读 references/source-and-guardrails.md 和 references/source-map.md。选品读 references/opportunity-map.md，工具/物流读 references/programs-and-logistics.md，广告读 references/lifecycle-advertising.md。
工作流
1. 固定细分类目与站点
记录 marketplace、product type、九大赛道之一、目标人群/空间/车辆/宠物、尺寸重量、供电/儿童/食品接触/材料/户外等风险、季节和价带。不要按“大类”套一个模型。
2. 建立当前机会证据
手册中的绿色环保、智能升级、情绪价值及各站趋势/推荐只进 SOURCE_CANDIDATE。用当前目标站点查询、点击/销量代理、评论、竞争、价格、退货和新品速度重验。
3. 类目硬闸门
- 家居/家具：尺寸、承重、材料、组装、空间与大件配送；
- 家装：电气/水路/施工与当地规范；
- 厨房：食品接触、温度、涂层、清洁和安全；
- 汽配：车型/年款/配置 fitment 与安装；
- 花园/运动：户外、电池、机械与人身安全；
- 玩具：年龄分级、儿童安全、小部件与测试；
- 宠物：物种/体型、摄入/夹伤/逃逸和卫生风险。
证据不足直接 HOLD。
4. 计算物流与经济性
包含体积重、头程、仓储、FBA/AWD/SFP 或其它当前可用方式、组装/破损、退货、备件、维修、季节库存和多变体。课程服务案例不替代当前费用/资格。
5. 选择当前可用增长工具
按业务问题核验 A+ Gen AI、Creator Connections、Climate Pledge Friendly、Amazon Custom、Part Finder/ACES 等资格。工具只解决具体问题，不用资格徽章替代产品证明。
6. 按生命周期做广告
家居、汽配和家具在课程中呈现不同节奏；实际阶段由当前 ASIN 数据决定。每阶段选一个主目标，建立搜索、商品投放、创意与利润护栏。
7. 单变量验证
只测一个产品/本地场景、fitment 说明、内容模块、物流方案、关键词/ASIN 或广告结构变量。所有外部写入逐对象审批。
必须交付
- 细分类目、站点、场景和风险作用域；
- 当前需求与源候选对照；
- 安全/fitment/合规、物流、利润与售后闸门；
- 可用项目与不适用项目；
- 生命周期广告和一个实验；
GO / HOLD / REJECT。
What ships with it
6 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.
- 1mo ago First seen · 60 lines · 162 tokens per session scan A fd0d79338c8a
sealeap-amazon-home-lifestyle-category-growth is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 162 tokens to every session and 931 once invoked, about $0.0006 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 2026-09-04.
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