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-operator-to-owner-readinessgit 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-operator-to-owner-readiness)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-operator-to-owner-readiness"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-operator-to-owner-readiness/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-operator-to-owner-readiness"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-operator-to-owner-readiness.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 | $0.00867 |
| Opus 5 | $0.00039 | $0.00434 |
| Sonnet 5 | $0.00015 | $0.00173 |
| Haiku 4.5 | $0.00008 | $0.00087 |
Grade A, and why
sealeap-amazon-operator-to-owner-readiness 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 运营创业准备度
目标
识别“会运营”与“能经营完整生意”之间的差距，先验证现金流、产品组合和全盘能力再决定是否独立。
适用任务
- 运营准备离职创业。
- 独立后新品成功率低、现金紧张或固定成本过高。
- 比较不同站点、商品类型和低固定成本模式。
开始前要拿到
- 个人与团队能力、可使用但不侵权的资源、时间和风险偏好。
- 启动资金、固定支出、生活备用金、供应链账期和回款周期。
- 候选商品单位经济、失败概率、补货和退出成本。
缺失的数据要明确列为缺口，并把结论标成事实、估算或假设；不要补造数字。
不可妥协的边界
- 不得带走前雇主的账号、数据、客户、供应商机密、代码或知识产权。
- 不把过去在公司获得的成绩等同于独立经营成功率。
- 不建议用生活必需资金或不可承受债务支撑高风险验证。
- 当前 Amazon 官方政策、帮助页、账户资格和后台实际字段优先于本 Skill 中的经验框架；规则可能变化时先核验。
- 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。
- 不输出原素材的创作者身份、账号、链接、视频编号或可反查线索；当前业务证据的官方来源、采集时间和口径仍需保留。
工作流
1. 拆分能力栈
分别评估选品、采购、财务、合规、广告、库存、客服和团队管理，找出过去由公司承担的部分。
2. 建立资金时间线
把公司注册、样品、生产、运输、入仓、推广、退货和回款排在同一现金流线上。
3. 建模组合成功率
根据单品验证成本和保守成功率估算需要多少候选品、总资金和时间，避免押注单一爆款。
4. 压低固定成本
比较居家、小团队、外包和逐步招聘方案，先让盈利 SKU 支撑扩张。
5. 选择适配战场
按自身供应链、语言、合规和竞争优势比较站点与细分市场，不因焦虑频繁换赛道。
6. 设置决策闸门
为离职、首批采购、第二轮补货和招聘分别设前置条件与退出线。
判断标准
- 现金模型包含个人生活跑道和企业营运资金。
- 能力评估不依赖前雇主的受保护资源。
- 每个扩张动作由盈利或已验证证据支撑。
必须交付的结果
- 运营到经营者能力差距表。
- 现金流与组合成功率模型。
- 低固定成本启动方案。
- 离职或继续准备的条件式结论。
结尾列出数据窗口、关键假设、证据缺口、风险和下一步；如包含待执行动作，单独放在“待批准变更”中。
What ships with it
1 file 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 · 75 lines · 77 tokens per session scan A b9046fb74a19
sealeap-amazon-operator-to-owner-readiness 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 867 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-pricing
Set competitive prices and model profitability. Use for Buy Box pricing analysis, profit margin calculation, breakeven analysis, or multi-marketplace pricing strategy.
ai-api-alternative-ai-hive
A billing-comparison guide for moving between AI API providers, which are services that let software send requests to AI models.
ai-api-live-price-snapshot-ai-hive
A guide for recording current AI model prices and setting spending limits before submitting generation jobs.
ai-hive-boss-coupon-cost
A coupon-cost analysis workflow that examines discount rules, redemption, stacking, profit margin, and purchases that might have happened without the coupon.
asc-ppp-pricing
Set territory-specific pricing for subscriptions and in-app purchases using current asc setup, pricing summary, price import, and price schedule commands. Use when adjusting prices by country or implementing localized PPP strategies.
ai-slop
Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside…