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-xiezhi-amazon-seasonal-blue-ocean-screeninggit 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-xiezhi-amazon-seasonal-blue-ocean-screening)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-seasonal-blue-ocean-screening"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-seasonal-blue-ocean-screening/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-xiezhi-amazon-seasonal-blue-ocean-screening"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-seasonal-blue-ocean-screening.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 | $0.00063 | $0.01242 |
| Opus 5.5 | $0.00025 | $0.00497 |
| Sonnet 5.5 | $0.00013 | $0.00248 |
| Haiku 4.5 | $0.00006 | $0.00124 |
Grade A, and why
sealeap-xiezhi-amazon-seasonal-blue-ocean-screening 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 季节性冷门机会筛选
目标
从历史月份和未来进场窗口中找出有明确需求、直接竞品较少且能继续验证的候选细分市场。
适用任务
- 按未来旺季倒排选品、生产、运输和上架时间。
- 从大量商品中初筛低评论、适中销量和较高客单价机会。
- 判断一个候选是可研究的细分市场，还是偶发销量或不可复制样本。
开始前要拿到
- 目标站点、计划上架日期、供应链交期与物流时效。
- 价格下限、毛利底线、首批库存上限和可承受广告成本。
- 历史月份的销量、评论、价格、变体和上架时间代理数据。
- 候选产品的精准词、直接竞品与需求场景。
缺少字段时列出证据缺口，并把相关结论标为 FACT、ESTIMATE、ASSUMPTION 或 UNKNOWN；不要补造数据。
不可妥协的边界
- 第三方数据均为估算或代理证据；Amazon 一方报告、后台实时字段和产品事实优先。
- 经验阈值只能作为可调起点，必须展示敏感性分析，不能写成 Amazon 官方规则。
- 不得捏造销量、搜索量、CPC、CVR、成本、认证、产品属性或消费者需求。
- 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。
- 不输出或保存素材来源身份、账号、链接、作品编号、互动数据、原始话术或其他可反查来源的线索。
- 不得把筛选结果直接当作可采购产品。
- 不得复制竞品设计；进入采购前必须完成知识产权、产品安全和平台政策核查。
工作流
1. 倒排窗口
从预期需求高峰向前倒排，至少为上架、入仓、评价与广告学习预留缓冲；先排除已经错过窗口的方向。
2. 分层初筛
用评论、销量、价格和历史月份做宽松筛选，并同时保留多组阈值做敏感性分析，避免单一参数制造假蓝海。
3. 验证精准需求
确认至少存在能描述产品属性、对象或场景的精准搜索词，并据此建立真正的直接竞品集合。
4. 检查可复制性
排查异常评论、极端促销、外部爆量、合并变体和短期事件；解释销量来源后再保留候选。
5. 形成候选卡
记录需求驱动、竞争强度、预计 CPC、保守 CVR、单位经济、进场时间和下一步差异化验证。
判断标准
- 评论上限 30/100/200、销量上限 500 等仅是探索分层，不是平台规则或通用答案。
- 优先验证未来三到五个月可能进入高峰的需求，但实际提前量必须由交期和站点时效倒推。
- 低评论且稳定出单只是线索；必须排除异常运营和不可持续流量。
- 候选在纯付费流量的保守情景下至少应接近盈亏平衡，否则标记 HOLD。
第三方 MCP 数据
需要外部关键词、竞品、评论或公开网页证据时，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。 - 凭证只从环境变量读取，不进入参数、URL、Skill、终端输出或 Git。
- 可能计费的
tools/call先展示 Provider、工具、无密钥参数、预计成本与输出位置，核对已有授权；仅在授权覆盖本次范围时使用--allow-cost，该标志不是费用上限。 - 脱敏结果用
--output写入 Skill 包之外的任务私有目录；不假设安装位置受仓库.gitignore保护。第三方数据标为估算或代理证据。 - 失败一次后记录缺口，不以重复付费重试掩盖不可用状态。
必须交付的结果
- 候选细分市场清单
- 直接竞品与精准词表
- 进场时间倒排表
- 单位经济与风险卡
- 继续研究或淘汰结论
结尾列出站点、数据窗口、证据来源、关键假设、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.
- 1mo ago First seen · 87 lines · 63 tokens per session scan A 94837900cebf
sealeap-xiezhi-amazon-seasonal-blue-ocean-screening is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,242 once invoked, about $0.0003 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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