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-amazon-global-growth-planninggit 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-amazon-global-growth-planning)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-global-growth-planning"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-global-growth-planning/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-amazon-global-growth-planning"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-global-growth-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00152 | $0.00862 |
| Opus 5.5 | $0.00061 | $0.00345 |
| Sonnet 5.5 | $0.00030 | $0.00172 |
| Haiku 4.5 | $0.00015 | $0.00086 |
Grade A, and why
sealeap-amazon-global-growth-planning 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 全球增长与 AI 运营规划
目标
将“用 AI 做全球化”拆成有人负责、可验证、可暂停的运营链：AI 负责发现、整理和起草，人负责商品事实、经济性、合规、品牌和外部写入；站点与节日机会只有通过当前证据闸门才进入执行。
开始前读 references/source-and-guardrails.md。建立 AI 工作流读 references/ai-operating-model.md，规划时机读 references/opportunity-calendar.md。
工作流
1. 固定业务结果
选择一个 90–180 天结果：验证新站点、缩短 Listing 周期、提高补货准确性、降低广告浪费、建立新品管线或降低合规风险。不要以“部署 AI”作为结果。
2. 画当前流程
按洞察、选品、供应链、Listing/本地化、上架、广告、库存、客服/复购、风险分段，记录输入、系统、负责人、决策、外部写入和失败成本。
3. 给 AI 分配角色
ASSIST：总结、翻译、格式化；ANALYZE：在有证据的数据上诊断；DRAFT：生成可审核方案；RECOMMEND：给出候选与证据/风险；EXECUTE_AFTER_APPROVAL：仅在逐对象批准后调用受保护操作。
高风险环节不得从 DRAFT 自动跳到 EXECUTE。
4. 选择站点/节日机会
白皮书与海报只生成候选。逐项验证当前日期、当地需求、竞争、product type、合规、库存/交期、本地化、贡献毛利和广告资格。用 references/opportunity-calendar.md 输出 GO / HOLD / REJECT。
5. 构建证据链
每个决策保存来源、时间、站点、主体/账户、商品、原始指标、计算、假设和审批。外部工具不可用时把空缺写成 gap，不让模型补数字。
6. 设计最小工作流
先实现一个可逆闭环：读数据 → 粗筛 → 模型筛选 → 人工审核 → 草案 → 单对象批准 → 写后复读。保留被拒候选和原因，便于审计与迭代。
7. 衡量能力与业务
同时报告：处理时间、人工返工、事实/合规错误、建议采纳率，以及收入、贡献利润、库存、退货、广告效率等业务结果。生产效率不能替代业务效果。
必须交付
- 明确业务结果与当前流程；
- AI 角色/权限矩阵和人工闸门；
- 站点/节日候选及当前证据；
- 一个最小可逆工作流和失败处理；
- 能力指标与业务指标；
DRAFT / PILOT_READY / HOLD状态。
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 · 58 lines · 152 tokens per session scan A f292420a9ca2
sealeap-amazon-global-growth-planning is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 152 tokens to every session and 862 once invoked, about $0.0006 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 2026-09-04.
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