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-keyword-ranking-experimentgit 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-keyword-ranking-experiment)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-keyword-ranking-experiment"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-keyword-ranking-experiment/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-keyword-ranking-experiment"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-keyword-ranking-experiment.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 | $0.00088 | $0.01108 |
| Opus 5 | $0.00044 | $0.00554 |
| Sonnet 5 | $0.00018 | $0.00222 |
| Haiku 4.5 | $0.00009 | $0.00111 |
Grade A, and why
sealeap-amazon-keyword-ranking-experiment 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 5d 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 关键词排名实验
目标
用广告实验筛选与商品最匹配、在合理位置能产生利润的关键词，再按证据逐级扩大，而不是把排名当作可直接购买的结果。
适用任务
- 从已收录词中选择优先推进对象。
- 估算某查询在不同广告位的订单潜力。
- 广告有订单但自然位置停滞。
开始前要拿到
- 关键词相关性、查询报告、自然位置历史和广告位表现。
- CTR、CVR、CPC、CPA、价格、库存和贡献利润。
- 竞品环境、促销和 Listing 变化时间线。
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
- Sponsored 位置与 organic 位置的曝光和点击机制不同，不能直接等同。
- 自然排名由 Amazon 系统决定；不承诺固定订单量或固定时间换取位置。
- 不得使用分时出价、广告或其他方法配合虚假订单和人为转化。
- 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. 筛选候选词
从真实相关、已有转化、自然可见度较好或广告效率较高的词中建立候选集，不只按当前名次。
2. 设定实验位置假设
选择一个或多个广告位类别，固定商品页、价格和预算，明确不是精确页码控制。
3. 估算订单潜力
比较各位置的曝光、CTR、CVR、CPA 和贡献利润，形成区间而非把广告订单直接当作未来自然订单。
4. 先推高把握词
优先给高意图长尾或中部词稳定预算；达到利润和样本门槛后才扩大。
5. 诊断自然停滞
检查相对转化、点击、库存、价格、竞争和词根相关性；不要仅靠更高竞价追自然位。
6. 迭代到核心词
当多个相关词形成稳定基本盘后，小规模测试核心词，并保留止损和退出路径。
判断标准
- 订单潜力以区间和假设呈现。
- 广告与自然数据分别展示。
- 候选词排序同时考虑利润和相关性。
必须交付的结果
- 候选词评分表。
- 广告位实验与预算护栏。
- 长尾、中部、核心词的推进顺序。
- 自然停滞诊断和下一步建议。
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.
- 5d ago First seen · 85 lines · 88 tokens per session scan A ed38b8b57ff8
sealeap-amazon-keyword-ranking-experiment is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 88 tokens to every session and 1,108 once invoked, about $0.0004 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 2026-09-07.
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