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-account-compliance-experiencegit 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-account-compliance-experience)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-yinglong-amazon-account-compliance-experience"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-yinglong-amazon-account-compliance-experience/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-account-compliance-experience"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-yinglong-amazon-account-compliance-experience.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 | $0.00077 | $0.00849 |
| Opus 5 | $0.00039 | $0.00425 |
| Sonnet 5 | $0.00015 | $0.00170 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
sealeap-yinglong-amazon-account-compliance-experience 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.
What it actually says
Amazon 账户、合规与风险：经营经验
目标
围绕经营经验，识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料。
使用范围
- 优先处理账户、合规与风险任务；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
- 风险分级
- 证据与时间线
- 根因分析
开始前要拿到
- 主体、站点、账户和受影响范围。
- 平台通知、政策原文、截止日期与 case。
- 注册、身份、支付、税务和权利证明。
- 操作时间线与责任人。
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
- 冻结猜测和高风险操作，保存通知与现状证据。
- 按主体、站点、政策条款和时间线复原问题。
- 区分可验证事实、平台判断、第三方解释和未知项。
- 准备最小、真实、一致的纠正与预防材料。
- 经人工或专业复核后提交，并跟踪 case、截止日期和复发条件。
专项路由
- 经营经验：把个人观点降级为待验证假设，并明确适用前提和反例。
完整的 1 张主题证据卡见 references/topic-cards.md；1 种原有组合见 references/scenario-patterns.md。
必须交付的结果
- 风险分级
- 证据与时间线
- 根因分析
- 纠正预防计划
- 申诉或复核材料草稿
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
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.
- 5d ago First seen · 66 lines · 77 tokens per session scan A 5c5ef87657a1
sealeap-yinglong-amazon-account-compliance-experience is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 77 tokens to every session and 849 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
ecom-compliance
Check product compliance, HS codes, IP risks, and platform requirements. Use for category approval, FDA/FCC/CE documentation, IP infringement screening, or dangerous goods classification.
ai-gaochao-cn-migration-ai-hive
A migration-review workflow for moving AI API workloads between providers. It examines data retention, training use, data location, deletion, contracts and operational tests before any traffic is moved.
ai-content-disclosure-check-ai-hive
A review workflow for deciding what AI-generated images, videos or text should disclose before publication. It considers how content was made, who appears in it, where it will be published and which region’s rules apply.
einbeziehung-online-clickwrap-browsewrap
Für Einbeziehung Online Clickwrap Browsewrap: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.
plattform-online-gate-rollout-rangfolge
Für Plattform und Online Checkout: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: plattform-online-gate-rollout-rangfolge.
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.