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-seller-entry-assessmentgit 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-seller-entry-assessment)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-seller-entry-assessment"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-seller-entry-assessment/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-seller-entry-assessment"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-seller-entry-assessment.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 | $0.00091 | $0.01142 |
| Opus 5.5 | $0.00036 | $0.00457 |
| Sonnet 5.5 | $0.00018 | $0.00228 |
| Haiku 4.5 | $0.00009 | $0.00114 |
Grade A, and why
sealeap-amazon-seller-entry-assessment 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 卖家入场可行性评估
目标
把“还能不能做”转化为团队自身条件、商品经济性和可验证市场假设的决策，而不是用个别成功案例代替分析。
适用任务
- 新卖家、工厂或运营团队准备进入 Amazon。
- 比较高低客单、精品与精铺、小团队与扩张模式。
- 评估当前资金是否足以度过开发、生产、运输和验证周期。
开始前要拿到
- 团队经验、可投入时间、供应链能力、合规能力和失败承受度。
- 产品售价、成本、费用、退货、广告 CPC/CVR 和库存周转假设。
- 市场规模、竞争、差异化、知识产权和资质门槛。
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
- 高客单并非天然更优；必须同时验证转化、退货、资本占用和售后成本。
- 不把低价、朋友成功或工厂供货优势单独当作进入理由。
- 不建议借贷或投入不可承受资金，也不提供收益保证。
- 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。
第三方 MCP 数据
只有在本任务确实需要外部市场、竞品、关键词或公开网页证据时，才读取 references/mcp-data-plan.md，并使用 scripts/mcp_research.py。
- 先动态执行 tools/list、search-tools 和 describe，依据实时 inputSchema 构造参数，不照搬历史工具名。
- 凭证只从环境变量读取，不放进命令参数、URL、Skill、结果文件或 Git。
- tools/call 可能计费。调用前展示 Provider、工具名、无密钥参数、预计成本与输出位置，核对已有授权覆盖后才加 --allow-cost；该标志不是费用上限。
- 第三方数据标为估算或代理证据，记录 Provider、工具、无密钥参数、查询时间和原始结果位置；失败一次后记录缺口，不反复消耗额度。
- 脱敏结果用 --output 写到 Skill 包之外的任务私有目录；不假设安装位置受仓库 .gitignore 保护，不把运行结果写入 Skill 包。
工作流
1. 评估创始条件
列出可迁移能力、缺口、时间投入和关键人依赖，区分运营经验与经营全盘能力。
2. 建立单位经济模型
从净售价扣除平台费、物流、广告、退货、税费和 COGS，计算贡献利润和盈亏平衡指标。
3. 测现金周期
覆盖备货、海运或空运、入仓、推广、回款和补货，计算基准与压力情景下的资金峰值。
4. 验证市场匹配
用多个可比产品、需求趋势和消费者痛点验证差异化，不照抄单一竞品。
5. 设计小规模试点
用少量 SKU、明确验证期和停止线先完成从零到一；成功后再复制，不提前扩团队和固定成本。
6. 形成决策
按产品、资金、能力、合规和时机分别评分，输出 GO、CONDITIONAL GO 或 NO-GO。
判断标准
- 利润和现金流同时为正才算健康，不用销售额替代。
- 关键假设都有验证方法和失败退出路径。
- 模式选择与团队真实优势相匹配。
必须交付的结果
- 入场能力与缺口表。
- 单位经济和现金流压力测试。
- 最小验证方案。
- 带前置条件的决策结论。
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
3 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 · 85 lines · 91 tokens per session scan A 6ae8d351fef4
sealeap-amazon-seller-entry-assessment is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 1,142 once invoked, about $0.0004 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 2026-09-07.
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