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-scenario-keyword-product-discoverygit 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-scenario-keyword-product-discovery)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-scenario-keyword-product-discovery"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-scenario-keyword-product-discovery/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-scenario-keyword-product-discovery"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-scenario-keyword-product-discovery.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 | $0.00058 | $0.01135 |
| Opus 5 | $0.00029 | $0.00567 |
| Sonnet 5 | $0.00012 | $0.00227 |
| Haiku 4.5 | $0.00006 | $0.00113 |
Grade A, and why
sealeap-xiezhi-amazon-scenario-keyword-product-discovery 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 场景词选品
目标
先找到可搜索的具体场景，再为该场景中的消费者挑选产品，从而把大众商品迁移到更清晰的细分需求。
适用任务
- 从陌生商品标题提取场景入口。
- 用场景词跨类目生成候选。
- 验证场景需求和付费流量盈利可能性。
开始前要拿到
- 目标站点、计划进场窗口和可接受类目。
- 近期上架商品的标题、属性、评论与场景描述。
- 场景词的搜索结果、价格、评论和销量代理数据。
- 目标毛利、供应链与合规限制。
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。
- 不得用不真实场景、虚假适用性或受保护活动标识包装普通产品。
- 找到场景不是立项完成，仍需全链路证据。
工作流
1. 广撒网找特征
浏览近期上架且结构、对象或用途较特殊的商品，目的只是发现不熟悉的语言和场景。
2. 提取场景词
从标题与详情中抽取使用对象、地点、活动、节日和任务词，并翻译为当地消费者真实表达。
3. 验证词义
在前台检查搜索结果是否主要服务同一场景；结果混杂时继续加属性或对象限定。
4. 跨类目扩展
用已验证场景词搜索不同产品形态，再按价格、评论、销量和供应链能力形成候选集。
5. 完成深研
逐个候选验证直接竞品、CPC、CVR、差异化、IP、合规、交期与首批库存。
判断标准
- 场景词必须能指向明确购物任务；仅有流量但结果混杂的词不能作为细分入口。
- 价格约 30 美元以上、评论约 100 以下可作探索条件，但应按品类经济性调整。
- 产品重新定位后必须真实适合该场景，不能只改文案造成误导。
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
- 场景词清单
- 搜索相关性验证
- 跨类目候选池
- 场景—产品—竞品矩阵
- 深研与淘汰结论
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.
- 5d ago First seen · 86 lines · 58 tokens per session scan A 606de0da9532
sealeap-xiezhi-amazon-scenario-keyword-product-discovery is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 58 tokens to every session and 1,135 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.
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