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-xiezhi-amazon-ad-relevance-weight-bootstrapgit 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-xiezhi-amazon-ad-relevance-weight-bootstrap)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-ad-relevance-weight-bootstrap"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-ad-relevance-weight-bootstrap/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-xiezhi-amazon-ad-relevance-weight-bootstrap"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-ad-relevance-weight-bootstrap.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 | $0.00062 | $0.01186 |
| Opus 5.5 | $0.00025 | $0.00474 |
| Sonnet 5.5 | $0.00012 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
sealeap-xiezhi-amazon-ad-relevance-weight-bootstrap 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 广告相关性启动
目标
用精准购物意图、页面一致性和受控学习预算积累可解释的点击与转化信号，而不是依赖未经证实的权重公式。
适用任务
- 为新品设计前几天的广告学习计划。
- 诊断相同出价但曝光差异。
- 在提高竞价前检查关键词和 Listing 相关性。
开始前要拿到
- 目标 ASIN/SKU、站点和产品事实。
- 精准词、搜索结果相关性和 Listing 字段覆盖。
- 建议竞价、广告位、预算、盈亏 CPC 与止损。
- CTR、CVR、CPC、订单和归因窗口基线。
缺少字段时列出证据缺口，并把相关结论标为 FACT、ESTIMATE、ASSUMPTION 或 UNKNOWN；不要补造数据。
不可妥协的边界
- 第三方数据均为估算或代理证据；Amazon 一方报告、后台实时字段和产品事实优先。
- 经验阈值只能作为可调起点，必须展示敏感性分析，不能写成 Amazon 官方规则。
- 不得捏造销量、搜索量、CPC、CVR、成本、认证、产品属性或消费者需求。
- 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。
- 不输出或保存素材来源身份、账号、链接、作品编号、互动数据、原始话术或其他可反查来源的线索。
- 广告写操作须在用户明确授权的对象、动作与预算范围内，并保留回退值；已有授权覆盖时不重复索取。
- 不得把经验性拍卖解释或短期相关性写成 Amazon 官方公式。
工作流
1. 建立相关性地图
将产品核心属性、对象和场景与搜索词、Listing 标题/要点和目标页面逐项对齐。
2. 选择启动词
优先购买意图明确且搜索结果高度一致的词，排除大而泛、与产品弱相关的流量。
3. 设置学习护栏
以建议竞价区间为参考制定小范围测试，预先限定日预算、累计花费、最低样本和停止条件。
4. 观察分层信号
分别看曝光、CTR、CVR、CPC、广告位和搜索词；先判断资格/相关性，再判断商品页和价格。
5. 逐步调整
一次只改变主要变量；只有转化证据支持时扩大预算或竞价，表现恶化则回退。
判断标准
- 广告排序受出价、相关性、预计效果和竞争环境共同影响；不存在可直接读取的固定单一权重分。
- 新品可进行受控学习，但不得无上限高价抢位或把前三天当作必然起量窗口。
- 盈亏 CPC = 广告前每单贡献毛利 × 订单 CVR；订单 CVR 按归因订单 / 点击，以小数代入。出价与实际 CPC 分开，并检查动态竞价和位置调整后的风险。
第三方 MCP 数据
需要外部关键词、竞品、评论或公开网页证据时，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。 - 凭证只从环境变量读取，不进入参数、URL、Skill、终端输出或 Git。
- 可能计费的
tools/call先展示 Provider、工具、无密钥参数、预计成本与输出位置，核对已有授权；仅在授权覆盖本次范围时使用--allow-cost，该标志不是费用上限。 - 脱敏结果用
--output写入 Skill 包之外的任务私有目录；不假设安装位置受仓库.gitignore保护。第三方数据标为估算或代理证据。 - 失败一次后记录缺口，不以重复付费重试掩盖不可用状态。
必须交付的结果
- 关键词—页面相关性矩阵
- 启动广告草案
- 预算与累计止损
- 学习期监控表
- 待批准最小变更集
结尾列出站点、数据窗口、证据来源、关键假设、缺口、风险、下一步和所有待批准动作。证据不足时写 HOLD，不得包装成可直接执行。
执行细节、证据字段和质量检查见 references/playbook.md。
What ships with it
4 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 · 86 lines · 62 tokens per session scan A 110f21275d2c
sealeap-xiezhi-amazon-ad-relevance-weight-bootstrap is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 1,186 once invoked, about $0.0002 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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