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-niche-market-profitability-screengit 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-niche-market-profitability-screen)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-niche-market-profitability-screen"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-niche-market-profitability-screen/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-niche-market-profitability-screen"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-niche-market-profitability-screen.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 | $0.00055 | $0.01132 |
| Opus 5.5 | $0.00022 | $0.00453 |
| Sonnet 5.5 | $0.00011 | $0.00226 |
| Haiku 4.5 | $0.00006 | $0.00113 |
Grade A, and why
sealeap-xiezhi-amazon-niche-market-profitability-screen 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 冷门细分盈利筛选
目标
把冷门定义为有精准需求、可解释的正常销量和较低直接竞争，并用单位经济与库存边界验证。
适用任务
- 筛选小体量蓝海候选。
- 验证精准词、CPC 和市场友好度。
- 为冷门产品制定小批首发目标。
开始前要拿到
- 站点、类目、计划旺季和上架时间。
- 销量、评论、价格、上架时间与历史月份数据。
- 精准词、CPC、直接竞品和低评论样本。
- 落地成本、毛利、首批量与目标市场份额。
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。
- 不得将少量竞品自动解释为高需求低竞争。
- 最终采购前完成差异化、IP、安全、政策和供应商核查。
工作流
1. 设置多档筛选
用销量、评论、售价与历史月份构造宽中严三档，而不是只跑一个参数组合。
2. 验证精准词
确认词能清晰描述产品/对象/场景，且搜索结果主要是同一购买任务。
3. 评估竞争与广告
比较低评论和头部样本，用新品或弱历史信号的假设情景估算 CPC 与 CPA 区间；不把不可观测的“权重”当成已测事实。
4. 核算盈利
在不计自然流量下运行多档 CVR；只有能接近或达到目标贡献利润时继续。
5. 扩展与控货
用对象/场景词寻找相邻机会，并按可比正常样本与目标份额限定首批库存。
判断标准
- 销量上限约 200–500、评论 30–100、售价约 30 以上是探索区间，不是定义。
- 冷门不等于无搜索词；找不到消费者表达时更可能是需求不可触达。
- 首批量应由可达份额、销售窗口和清货能力决定，不由头部销量直接外推。
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
- 三档筛选结果
- 精准词与竞品集合
- 广告盈利情景
- 相邻细分机会
- 首批库存与 HOLD/GO 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 · 86 lines · 55 tokens per session scan A 6c5d449e8c08
sealeap-xiezhi-amazon-niche-market-profitability-screen is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 1,132 once invoked, about $0.0002 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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