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-yinglong-amazon-customer-feedback-reviewsgit 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-yinglong-amazon-customer-feedback-reviews)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-yinglong-amazon-customer-feedback-reviews"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-yinglong-amazon-customer-feedback-reviews/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-yinglong-amazon-customer-feedback-reviews"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-yinglong-amazon-customer-feedback-reviews.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 | $0.00086 | $0.01170 |
| Opus 5.5 | $0.00034 | $0.00468 |
| Sonnet 5.5 | $0.00017 | $0.00234 |
| Haiku 4.5 | $0.00009 | $0.00117 |
Grade A, and why
sealeap-yinglong-amazon-customer-feedback-reviews 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 客户反馈与转化改进：评价与口碑、Listing 诊断、AI 工作流
目标
围绕评价与口碑、Listing 诊断、AI 工作流，从评论、退货和客服证据定位购买障碍，形成产品或页面改进的优先级和验证计划。
使用范围
- 优先处理客户反馈与转化改进任务；从专项证据卡选择与当前对象和问题直接相关的主题。
- 集合名称用于维护文件归属，不限制用户要求的交叉验证。其他来源与业务域的证据须分别标注，再按同口径比较。
- 本 Skill 可独立使用，不依赖仓库中的私有语义稿。保留去标识化边界，不恢复原素材身份或逐条映射。
适用任务
- VOC 主题表
- 根因树
- 改进优先级
开始前要拿到
- 站点、ASIN/SKU、变体和时间窗。
- 合规取得的评论、退货原因、Q&A 和客服主题。
- 页面、价格、库存和广告流量结构。
- 产品与包装事实。
缺少字段时明确标为 UNKNOWN 或 NEEDS_EVIDENCE，不要补造数据。
不可妥协的边界
- 本 Skill 来自去标识化语义转译，不保留或推断素材来源身份，也不把素材观点冒充 Amazon 当前政策。
- 产品事实、账户事实和 Amazon 一方报告优先；第三方数据必须标明 Provider、站点、日期、样本和估算口径。
- 不捏造销量、搜索量、成本、合规状态、产品属性、评论、平台通知或执行结果。
- 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。
- 平台规则、费率、界面和资格会变化；执行前复核当前官方文档与后台状态。
工作流
- 清洗重复、变体错配和异常评论，保持样本口径。
- 按任务、障碍、后果、场景和人群聚类 VOC。
- 把问题映射到产品、包装、说明、Listing 或流量承诺。
- 按影响、频率、可实现性和受保护收益排序改进。
- 用单变量页面或样品实验验证，禁止操纵评价。
专项路由
- 评价与口碑：只分析合规获取的 VOC；禁止操纵评价、诱导好评或联系受限买家。
- Listing 诊断：从合规、可发现性、点击和转化四层审计当前详情页。
- 平台费用：以站点、日期、尺寸档和官方费率重算费用，保留账单证据。
- 点击表现：按搜索结果与广告位拆分点击数据，区分素材、价格、承诺和流量相关性问题。
- AI 工作流：把 AI 限定为有输入、证据、审核和回退的可复核流程。
- 运营工具：先核对工具的数据来源、权限、费用和导出字段，再决定是否接入。
- 库存管理：以销量速度、在途、交期和安全库存建立补货与断货预案。
- 变体关系：核对允许的变体主题、子体事实、评论关系与合并拆分风险。
完整的 40 张主题证据卡见 references/topic-cards.md；28 种原有组合见 references/scenario-patterns.md。
第三方数据（可选）
只有在用户数据或 Amazon 一方报告不足时，才按 references/mcp-data-plan.md 发现实时 schema、执行 dry-run，并在可能计费的调用前核对具体请求与预算授权；已有授权覆盖时不重复索取。凭证通过环境变量注入，结果保存到 Skill 包之外的任务私有目录。
必须交付的结果
- VOC 主题表
- 根因树
- 改进优先级
- 页面或产品实验卡
- 评价合规检查
结尾列出站点、时间窗、数据口径、证据、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
6 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 · 77 lines · 86 tokens per session scan A b23bebc73953
sealeap-yinglong-amazon-customer-feedback-reviews is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 1,170 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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