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-white-hat-product-rankinggit 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-white-hat-product-ranking)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-white-hat-product-ranking"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-white-hat-product-ranking/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-white-hat-product-ranking"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-white-hat-product-ranking.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 | $0.00083 | $0.01123 |
| Opus 5.5 | $0.00033 | $0.00449 |
| Sonnet 5.5 | $0.00017 | $0.00225 |
| Haiku 4.5 | $0.00008 | $0.00112 |
Grade A, and why
sealeap-amazon-white-hat-product-ranking 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 合规关键词覆盖扩张
目标
通过分层广告和真实转化扩大有效关键词覆盖，把主要出单词变成可独立管理的资产，并在成熟后收缩无效花费。
适用任务
- Listing 已具备竞争力且预算相对充足的新品。
- 希望同时扩大词量并精细管理主力词。
- 需要把促销纳入广告节奏但保持官方资格和利润边界。
开始前要拿到
- 关键词全集及头部、中部、长尾和词根分类。
- Listing readiness、库存、价格、Vine 或其他官方项目资格。
- 广告预算、促销成本、贡献利润和停止线。
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
- 评论只能来自真实客户或符合资格的官方项目；不得安排直评、测评或评论合并。
- 不得用大规模广告数量替代相关性和预算控制。
- 促销必须符合当前官方资格与价格规则，并先核算促销后贡献利润。
- 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. 确认可承接
检查商品信息、价格、库存、配送、合规和真实评价基础，未达标时先修 Listing 或产品。
2. 扩展收录入口
建立受控自动、相关词根广泛和高相似商品投放，分别定义探索对象和预算。
3. 保护转化
首轮不直接无差别冲头部词，优先覆盖中部词和高意图词根；不相关词根提前审慎否定。
4. 精准承接
当某词达到相关性、样本和利润门槛且在探索层预算不稳时，迁移到独立精准广告。
5. 分层放量
随着稳定词增多，再逐步扩展头部或更泛流量；符合资格时用官方促销做独立实验，避免同时改变过多变量。
6. 成熟收敛
按增量利润保留主力词，降低重复探索和低效广告，并持续监控库存与自然侧。
判断标准
- 广告数量由业务问题决定，不设人为数量目标。
- 每个迁移词有报告证据和去重方案。
- 放量后总利润、退货和库存仍在护栏内。
必须交付的结果
- 覆盖层、承接层和放量层架构。
- 关键词迁移与否定表。
- 促销实验和库存保护方案。
- 成熟期广告收敛计划。
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 · 83 tokens per session scan A 670a1b700b1b
sealeap-amazon-white-hat-product-ranking is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,123 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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