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-product-targetinggit 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-product-targeting)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-product-targeting"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-product-targeting/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-product-targeting"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-product-targeting.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 | $0.00168 | $0.02498 |
| Opus 5 | $0.00084 | $0.01249 |
| Sonnet 5 | $0.00034 | $0.00500 |
| Haiku 4.5 | $0.00017 | $0.00250 |
Grade A, and why
sealeap-amazon-product-targeting 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Ads 商品投放 · ASIN/品类定向与关键词联动
目标
把商品投放从“找一批竞品 ASIN 去打”升级为一条可复核的流量设计链：先还原商品与消费者任务，再识别关键词覆盖不到的商品页、类目节点、互补/替代和自家详情页流量，形成候选池，最后用独立 Campaign/Ad Group 做单变量验证。
源文件名与实际内容不一致：文件名是《如何提升关键词引流效率？》，但 39 页课件实际标题和正文均为《商品投放实用案例分享》。本 Skill 以实际内容为准，并保留关键词与商品投放联动部分。先读 references/source-and-guardrails.md。
不可妥协的边界
- 商品投放包括课程中的品类定向和 ASIN 定向；“扩展商品投放”、细化条件、否定能力、广告位和支持广告产品均以当前 marketplace 控制台/API 为准。
- 课件中标为“第三方卖家意见”的 3WCS、榜单分级、欧洲站贴标签、日本站反查词和阻力带案例只能作为
SELLER_HYPOTHESIS，不能写成 Amazon 官方机制。 - 不声称商品投放会让系统“收录关键词”、增加自然排名或给 ASIN 贴上确定标签。只观察可测的曝光、点击、订单、流量位置和利润变化。
- 不因为竞品是 FBM、自家是 FBA 就认定一定更有竞争力；必须比较当前价格、配送承诺、评分、评价量、变体、优惠和商品匹配。
- 不复制竞品文案、素材、商标表达或虚构比较优势；只使用公开商品事实与合法定向能力。
store_id、profile、ASIN 或 marketplace 不等于授权。读取与写入都必须绑定当前验证的服务端账户范围。- 不使用固定“点击 N 次无单”否定阈值。按利润、流量、归因窗口和统计证据定义停止规则。
- 默认只读和草案。任何 target、negative target、bid、budget、placement、status 或结构变更必须逐项人工确认。
先声明模式
RESEARCH：只读构建流量地图与候选池；默认；DIAGNOSE：诊断现有商品投放；DRAFT：生成分层结构与单变量实验；RELEASE_PREP：生成审批卡、旧值/新值、护栏和回退；APPROVED_WRITE：只执行用户本轮明确批准的一个动作，写后复读。
核心工作流
1. 锁定对象、目标和基线
记录：
- 已验证 seller、marketplace、广告 profile、广告产品、ASIN/SKU 与父子体；
- 目标只能选一个：
扩大覆盖 / 突破关键词瓶颈 / 类目节点 / 细分人群 / 交叉销售 / 升级销售 / 自家防御 / 竞品进攻； - 当前关键词、自动和商品投放结构及近 7/14/30 天表现；
- 贡献毛利、盈亏线、库存、Featured Offer、价格/优惠、评价与配送；
- 基线窗口、归因窗口、当前变更和季节事件。
缺少明确目标时先输出 NEEDS_DATA，不要把七种场景全部混在一个 Campaign。
2. 建立商品事实与 3WCS 假设表
用 references/use-cases-and-selection.md 建立：
What: 商品身份、功能、特性、材质、颜色、尺寸、售卖方式
Who: 真实购买对象与购买任务
Where: 使用场景
Competitor: 同需求、同价格带、可替代的竞品
Substitute: 关联、互补或替代商品
3WCS 来自第三方卖家观点。每一项都要绑定商品事实、账户查询或市场观察证据；不能凭想象填人群与场景。
3. 还原当前流量结构
至少获取：
- Campaign / Ad Group / Targeting / Search Term / Placement 报告；
- advertised product 与 purchased product 维度；
- 当前自动投放、手动关键词、手动商品投放及 negative targeting；
- 搜索结果与详情页的当前可见广告/自然位置观察；
- Brand Analytics、Search Query Performance 或账户可用的一方查询证据；
- 当前 Best Sellers / New Releases、类目节点与候选 ASIN 前台事实。
按 搜索流量 / 商品详情页 / 类目节点 / 互补 / 替代 / 自家 / 竞品 聚合曝光、点击、花费、订单、销售和贡献利润。不要把单个低样本 ASIN 当成稳定规律。
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 · 186 lines · 168 tokens per session scan A 4105699e0b52
sealeap-amazon-product-targeting is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 168 tokens to every session and 2,498 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-product-research
A Sorftime-based product research skill for finding Amazon market opportunities and deciding whether a new product is worth pursuing. Sorftime is a market-research data source for Amazon sellers.
zach-listing-health-checker
An Amazon listing health checker that examines a product page as a shopper would see it, including visibility, price, seller, cart, delivery, category, rank, reviews, and search visibility.
zach-sif-cvr-threshold-analyzer
An analyzer that compares daily conversion data for an Amazon product with its natural keyword rankings. CVR, or conversion rate, is the share of visitors who buy; natural rankings are unpaid search positions.
creative-generate
A workflow for creating advertising images and banners from a strategy brief. It separates the main visual artwork from the accurately typeset text and produces variants for display or social formats.
ecom-social
Instructions for creating and improving e-commerce social-media content, advertising, and community work across platforms such as Instagram, YouTube, TikTok, Pinterest, Reddit, WhatsApp, and Xiaohongshu.
ecom-compliance
Check product compliance, HS codes, IP risks, and platform requirements. Use for category approval, FDA/FCC/CE documentation, IP infringement screening, or dangerous goods classification.