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-conversion-diagnostic-laddergit 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-conversion-diagnostic-ladder)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-conversion-diagnostic-ladder"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-conversion-diagnostic-ladder/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-conversion-diagnostic-ladder"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-conversion-diagnostic-ladder.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 | $0.00052 | $0.01158 |
| Opus 5.5 | $0.00021 | $0.00463 |
| Sonnet 5.5 | $0.00010 | $0.00232 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
sealeap-xiezhi-amazon-conversion-diagnostic-ladder 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 转化率分层诊断
目标
先查商品与页面承接，再查广告流量，最后判断产品竞争力和可达市场份额，避免用调竞价掩盖根因。
适用任务
- 排查新品或老品低转化。
- 区分 Listing、价格、物流与流量问题。
- 判断继续优化还是止损退出。
开始前要拿到
- 搜索结果与详情页素材、价格、优惠、评分、变体和配送状态。
- 分搜索词、目标和广告位的 CTR、CVR、CPC、订单及花费。
- 同评分层级直接竞品的价格、销量和页面。
- 退货、差评、库存、Buy Box 和归因延迟信息。
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。
- 不得制造、筛选或操纵评价。
- Listing 和广告变量应分开测试，任何线上改动先批准。
工作流
1. 检查商品承接
依次核对主图可见差异、副图信息、评分质量、定价、变体、要点前段、视频、标题前段、库存和配送。
2. 检查广告位
比较顶部、其余搜索和商品页的 CPC/CVR/ACoS，在样本充分时把流量移向高质量位置。
3. 清理查询
从搜索词报告识别不相关属性、品牌和高点击弱相关词；高相关但低转化词先回查页面与价格。
4. 调整目标
将广泛中已验证的精准词独立测试，低效目标先降价或隔离，不在数据不足时一刀切。
5. 判断天花板
与同评论、同定位、同价格带竞品比较；若自身已不弱于同层级，重新评估目标规模而非盲目加流量。
判断标准
- 诊断顺序是商品/页面 → 流量 → 市场份额，不可直接从高 ACoS 跳到降竞价。
- 主图、标题、视频数量或评分星级与 CVR 的关系需用本 ASIN 数据验证，不能套用固定倍数。
- 高客单价和长决策产品需考虑归因延迟与更长观察窗。
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
- 九项承接审计
- 广告位与搜索词诊断
- 根因排序
- 最小实验方案
- 继续、限流或止损建议
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 · 52 tokens per session scan A da7f19106d81
sealeap-xiezhi-amazon-conversion-diagnostic-ladder is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 1,158 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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