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 | $0.00076 | $0.00431 |
| Sonnet 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 8d 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.
- 8d 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 6d ago), licensed MIT. It adds 152 tokens to every session and 862 once invoked, about $0.0008 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 2026-09-04.
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.
sorftime-seller-agent
Sorftime Seller Agent — Expert-level cross-border e-commerce data analysis and product sourcing intelligence for Amazon, Walmart, TikTok Shop, 1688, Shopee, and TEMU sellers (plus Reddit social-listening queries). A single skill that turns any MCP-enabled AI agent (Claude Code, OpenClaw, Cursor, Copilot) into a…
amazon-analysis
Amazon-domain general analysis and multi-endpoint research engine. Handles broad or composite Amazon research requests that span multiple data dimensions or have no single specialized angle. Use when: - user asks for multi-endpoint Amazon research, composite reports, or general Amazon market/product analysis user asks…
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…