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-efficiency-benchmarkgit 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-efficiency-benchmark)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/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-efficiency-benchmark"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark.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 | $0.00056 | $0.01181 |
| Opus 5.5 | $0.00022 | $0.00472 |
| Sonnet 5.5 | $0.00011 | $0.00236 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade A, and why
sealeap-xiezhi-amazon-ad-efficiency-benchmark 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 广告效率竞品基准
目标
用同口径竞品组比较广告覆盖与销量代理，识别低评论样本是否能靠少量精准流量正常出单，并把结论限定为代理指标。
适用任务
- 评估细分市场广告架构复杂度。
- 比较低评论和高评论竞品的广告效率。
- 识别由真实差异化带来的异常高效样本。
开始前要拿到
- 一个经过验证的精准词及其搜索结果。
- 直接竞品的父子体口径销量、评论与可见广告词。
- 关键词 CPC、匹配类型、广告位和时间窗。
- 候选产品售价、贡献毛利和 CVR 情景。
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。
- 不得把第三方抓到的广告词当作完整账户事实。
- 不得将无法解释的离群样本作为可复制标杆。
工作流
1. 定义竞品与口径
锁定相同任务、属性和价格带商品，统一父体/子体、销量与广告词统计口径。
2. 收集广告代理
统计可见的商品/品牌广告词或其他覆盖代理，并记录工具覆盖不足和时间点。
3. 计算相对效率
用销量代理/广告词数等指标比较同市场样本，不把该比率当作真实广告产出。
4. 按评论分层
比较低评论组与成熟组的分布，寻找多条正常低评论高效样本，而非只看离群点。
5. 解释差异
检查高效样本是否由精准属性、可见差异、价格、评分、变体或页面承接解释。
6. 连接单位经济
把精准词 CPC 与 CVR 区间带入保本计算，决定是否值得继续。
判断标准
- 广告效率代理 = 销量代理 / 可见广告词数；可见词数为零或缺失时记为不可计算，不能判成无限高效。只在工具覆盖、对象与窗口可比时比较，不代表真实广告 ROI。
- 低评论组接近成熟组是友好信号，不等于新品一定转化相同。
- 可见广告词少可能来自抓取遗漏、预算变化或季节时点，必须报告缺口。
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
- 竞品口径说明
- 广告覆盖与销量代理表
- 评论分层分布
- 高效样本归因
- 经济性与深研结论
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 · 90 lines · 56 tokens per session scan A 98d59f9db735
sealeap-xiezhi-amazon-ad-efficiency-benchmark is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,181 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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