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-asin-targeting-strategygit 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-asin-targeting-strategy)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-asin-targeting-strategy"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-asin-targeting-strategy/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-asin-targeting-strategy"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-asin-targeting-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00084 | $0.01078 |
| Opus 5.5 | $0.00034 | $0.00431 |
| Sonnet 5.5 | $0.00017 | $0.00216 |
| Haiku 4.5 | $0.00008 | $0.00108 |
Grade A, and why
sealeap-amazon-asin-targeting-strategy 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 ASIN 商品投放策略
目标
用少量高匹配商品目标验证详情页与搜索环境机会，并把被报告证实的查询或目标迁移到独立结构。
适用任务
- 新品希望用商品投放启动。
- 从大量竞品 ASIN 中筛选少量高价值目标。
- 商品投放有订单但预算与目标混杂。
开始前要拿到
- 候选 ASIN 的品类、规格、用途、价格、评分、配送和销量代理。
- 本品真实差异化、价格、评分、库存和内容承接力。
- 商品投放、搜索词和广告位报告。
缺失的数据要明确列为缺口，并把结论标成事实、估算或假设；不要补造数字。
不可妥协的边界
- 所谓转化优势必须来自真实价格、功能、内容或服务，不得来自虚假评论或人为参考价。
- 商品投放不能保证复制对方关键词，也不能精确承诺出现在某个自然页码。
- 不得使用无授权的竞品私密数据。
- 当前 Amazon 官方政策、帮助页、账户资格和后台实际字段优先于本 Skill 中的经验框架；规则可能变化时先核验。
- 默认提供诊断或草案。写入前展示对象、旧值、新值、影响、停止线与回退，核对用户已有授权是否覆盖对象、动作与预算；范围已明确授权时继续执行并回读核验，只有未覆盖或扩大的范围才请求批准。
- 不输出原素材的创作者身份、账号、链接、视频编号或可反查线索；当前业务证据的官方来源、采集时间和口径仍需保留。
第三方 MCP 数据
只有在本任务确实需要外部市场、竞品、关键词或公开网页证据时，才读取 references/mcp-data-plan.md，并使用 scripts/mcp_research.py。
- 先动态执行 tools/list、search-tools 和 describe，依据实时 inputSchema 构造参数，不照搬历史工具名。
- 凭证只从环境变量读取，不放进命令参数、URL、Skill、结果文件或 Git。
- tools/call 可能计费。调用前展示 Provider、工具名、无密钥参数、预计成本与输出位置，核对已有授权覆盖后才加 --allow-cost；该标志不是费用上限。
- 第三方数据标为估算或代理证据，记录 Provider、工具、无密钥参数、查询时间和原始结果位置；失败一次后记录缺口，不反复消耗额度。
- 脱敏结果用 --output 写到 Skill 包之外的任务私有目录；不假设安装位置受仓库 .gitignore 保护，不把运行结果写入 Skill 包。
工作流
1. 建立目标评分
按品类相关性、功能相似度、受众重合、需求、价格区间和本品真实优势筛选。
2. 控制初始范围
优先单目标或小同质组，关闭不必要的扩展，确保每个结果可归因。
3. 提出位置假设
根据广告位报告区分商品页面与搜索位置表现；调整基础竞价和位置系数前先计算有效出价。
4. 运行验证
记录点击、CVR、CPA、已购商品、搜索词和利润；无订单时先判断样本和相关性。
5. 迁移赢家
稳定商品目标独立管理；报告中出现且证据充分的高转化查询可迁移到精准关键词广告。
6. 扩大边界
只有首批结果成立后才增加更广目标，并持续检查内部重复与库存。
判断标准
- 每个候选 ASIN 都有入选或排除理由。
- 位置结论来自报告，不由主观前台观察单独决定。
- 迁移查询有实际报告证据。
必须交付的结果
- ASIN 目标评分与批次。
- 商品投放结构和竞价假设。
- 目标或查询迁移规则。
- 扩量与停止条件。
结尾列出数据窗口、关键假设、证据缺口、风险和下一步；如包含待执行动作，单独放在“待批准变更”中。
What ships with it
3 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 · 85 lines · 84 tokens per session scan A 6f5452880931
sealeap-amazon-asin-targeting-strategy is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 84 tokens to every session and 1,078 once invoked, about $0.0003 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 2026-09-07.
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