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-qinglong-amazon-listing-operations-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-qinglong-amazon-listing-operations-reviews)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-listing-operations-reviews"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-listing-operations-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-qinglong-amazon-listing-operations-reviews"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-listing-operations-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 | $0.00081 | $0.01171 |
| Opus 5 | $0.00041 | $0.00585 |
| Sonnet 5 | $0.00016 | $0.00234 |
| Haiku 4.5 | $0.00008 | $0.00117 |
Grade A, and why
sealeap-qinglong-amazon-listing-operations-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sealeap-yinglong-amazon-listing-operations-listing — 86% identical, 24 lines differ
What it actually says
Amazon Listing 与页面操作：评价与口碑、五点描述、转化率
目标
围绕评价与口碑、五点描述、转化率，定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划。
使用范围
- 优先处理Listing 与页面操作任务；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
- 页面问题矩阵
- 事实与关键词地图
- 字段级新旧对比
开始前要拿到
- 站点、ASIN、SKU、product type 与父子体。
- 当前页面字段和 Listing issues。
- 产品事实、包装与合规证据。
- 关键词、竞品和客户语言证据。
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
- 读取当前页面和实时 product type 规则，保存字段基线。
- 按合规、可发现性、点击和转化四层定位问题。
- 建立购买问题、事实证据和字段位置映射。
- 生成字段级草稿并校验字符、byte、图片和变体完整性。
- 先对字段草稿做只读预检；在明确授权范围内发布后回读页面与 issues。
专项路由
- 供应链：统一规格询价，核验产能、质量、交期、合规和备选方案。
- CPC：按关键词、广告位和匹配方式拆解点击成本，并与可承受 CPC 做差额分析。
- 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。
- 广告曝光：核对投放资格、相关性、竞价、预算、状态和流量入口，再定位曝光缺口。
- 五点描述：按购买问题组织收益、事实和适用边界，避免重复或虚假承诺。
- 转化率：把转化问题拆成流量意图、详情页说服力、价格评价、库存配送和购买障碍。
- A+ 页面：按购买决策顺序规划模块、证据、对比和 FAQ，并校验移动端。
- 图片与视频：建立信息优先级、事实证据、可读性和单变量创意测试。
完整的 34 张主题证据卡见 references/topic-cards.md；10 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
- 页面问题矩阵
- 事实与关键词地图
- 字段级新旧对比
- 验证预览结果
- 回退快照
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.
- 5d ago First seen · 77 lines · 81 tokens per session scan A 76139d635ec3
sealeap-qinglong-amazon-listing-operations-reviews is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 81 tokens to every session and 1,171 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.
Other skills, from other repositories
zach-seller-skill-creator
A Chinese-language guide for Amazon sellers who want to turn repeated work processes into reusable skills for an AI agent.
zach-search-term-analyzer
An analyzer for Amazon Brand Analytics Top Search Terms reports, which show popular searches across Amazon and how clicks and conversions are distributed among products.
zach-search-term-report-analyzer
An Amazon Ads search-term report analyzer for Sponsored Products, Sponsored Brands, and Sponsored Display campaigns. It groups related search terms, measures results over 7, 14, and 30 days, and produces reports in several file formats.
amazon-market-trend-scanner
Amazon category trend scanner. Scans Amazon category landscapes to discover trending subcategories, emerging niches, and market shifts. Tracks demand surges, brand consolidation, new entrant waves, price band migration, and margin changes across all subcategories under a parent category. Use when user asks about…
amazon-pricing-command-center
Data-driven pricing strategy engine for Amazon sellers. Given one or more ASINs, auto-detects each product's leaf category, analyzes the pricing landscape, and delivers RAISE/HOLD/LOWER signals with profit simulation. Supports single ASIN or batch (multiple ASINs, auto-grouped by category). Uses ZooData API endpoints…
ecom-applicability
Determine whether AI is appropriate for a specific e-commerce task. Use when evaluating if a problem has enough data, the right tools, or acceptable risk for AI automation. Answers 'should I use AI for X?' with boundary-aware reasoning.