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-cross-category-attribute-keyword-researchgit 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-cross-category-attribute-keyword-research)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-cross-category-attribute-keyword-research"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-cross-category-attribute-keyword-research/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-cross-category-attribute-keyword-research"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-xiezhi-amazon-cross-category-attribute-keyword-research.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 | $0.00060 | $0.01120 |
| Opus 5.5 | $0.00024 | $0.00448 |
| Sonnet 5.5 | $0.00012 | $0.00224 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
sealeap-xiezhi-amazon-cross-category-attribute-keyword-research 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 跨类目通用词选品
目标
以消费者可搜索的通用属性为入口跨类目发现需求，再用盈利与竞争门槛收敛，而不是依赖一套固定参数。
适用任务
- 用材质、工艺、元素或场景词跨类目找产品。
- 把一个供应链能力映射到多个细分需求。
- 从大结果集中筛出低评论可盈利方向。
开始前要拿到
- 一个经过本地化验证的通用词。
- 全站点包含该词的商品、类目、价格、评论和销量数据。
- 目标利润、广告成本和进场时间。
- 供应链可做材质、工艺、图案和数量范围。
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。
- 热点元素、角色、图案、文字和品牌词必须先做 IP 核查。
- 词出现在标题中不等于需求成立，仍要验证搜索意图。
工作流
1. 定义通用词
从材质、工艺、属性、主题、人群、场景、节日或活动中选择能跨产品复用的搜索表达。
2. 全站发现
不预设单一类目，检索包含该词的商品并按产品形态与需求场景聚类。
3. 应用经济筛选
再以评论、价格、历史月份、预计利润和旺季窗口缩小范围，保留不同阈值结果。
4. 验证精准性
逐簇检查词与商品是否高度相关、是否存在真实直接竞品及正常低评论样本。
5. 进入立项
对候选补齐 CPC/CVR、差异化、供应链、IP、合规和库存证据。
判断标准
- 固定参数会限制视野；先用通用词发现，再根据产品经济和风险收敛。
- 评论不超过约 50、售价不低于约 25 等只可作探索起点。
- 跨类目复用的是能力和需求语言，不是直接复制产品。
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
- 通用词定义卡
- 跨类目商品聚类
- 筛选敏感性表
- 精准需求验证
- 立项候选清单
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 · 60 tokens per session scan A 7d766aa2fcd9
sealeap-xiezhi-amazon-cross-category-attribute-keyword-research is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,120 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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