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-three-factor-opportunity-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-three-factor-opportunity-screen)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-three-factor-opportunity-screen"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-three-factor-opportunity-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-three-factor-opportunity-screen"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-three-factor-opportunity-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 | $0.00057 | $0.01098 |
| Opus 5 | $0.00028 | $0.00549 |
| Sonnet 5 | $0.00011 | $0.00220 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
sealeap-xiezhi-amazon-three-factor-opportunity-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 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 三因子机会粗筛
目标
用销量、价格和评论三维启发式快速减少样本，再对陌生细分机会做严格二次验证。
适用任务
- 对大类目做快速机会扫描。
- 寻找低评论仍稳定出单的中小体量产品。
- 为下一轮市场与差异化研究建立候选池。
开始前要拿到
- 目标类目与站点。
- 月销量、价格、评论、上架时间和商品特征代理数据。
- 目标毛利、物流与采购限制。
- 排除词和不可做品类。
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。
- 不得在粗筛后直接下单。
- 不得把市场估算写成真实销售或平台官方数据。
工作流
1. 设置起点
以“销量上限、售价下限、评论上限”构成探索起点；阈值按类目分位数、单位经济和风险偏好设定，而非套用固定组合。
2. 观察三类样本
优先看陌生品类、低评论稳定出单链接和可通过组合/数量/场景重构的商品。
3. 动态调参
结果太少时逐项放宽；结果太多时增加上架时间、类目或排除词，并记录每次变化。
4. 排除异常
检查合并评论、异常评价、极端低价、外部强推、不可持续事件和直接竞品定义。
5. 转入深研
为保留方向补齐需求、CPC、CVR、单位经济、供应链、IP 和合规证据。
判断标准
- 三个阈值用于压缩候选集，不是成功公式；报告必须给出至少一组放宽和一组收紧后的敏感性结果。
- 筛选器的作用是发现，而不是替代市场判断。
- 高客单价、低评论或中小销量单独出现都不能证明机会。
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
- 筛选参数记录
- 候选商品池
- 异常排除表
- 陌生需求学习卡
- 深研优先级
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.
- 5d ago First seen · 86 lines · 57 tokens per session scan A 9592220e9b35
sealeap-xiezhi-amazon-three-factor-opportunity-screen is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 57 tokens to every session and 1,098 once invoked, about $0.0003 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
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.
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…
amazon-pricing-command-center
Data-driven pricing strategy engine for Amazon sellers. Given one or more ASINs, auto-detects each product's leaf category, analyzes the pricing landscape, and delivers RAISE/HOLD/LOWER signals with profit simulation. Supports single ASIN or batch (multiple ASINs, auto-grouped by category). Uses ZooData API endpoints…