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-amazon-keyword-selection-and-campaign-mappinggit 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-amazon-keyword-selection-and-campaign-mapping)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-keyword-selection-and-campaign-mapping"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-keyword-selection-and-campaign-mapping/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-amazon-keyword-selection-and-campaign-mapping"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-keyword-selection-and-campaign-mapping.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 | $0.00084 | $0.01200 |
| Opus 5.5 | $0.00034 | $0.00480 |
| Sonnet 5.5 | $0.00017 | $0.00240 |
| Haiku 4.5 | $0.00008 | $0.00120 |
Grade A, and why
sealeap-amazon-keyword-selection-and-campaign-mapping 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 关键词选取与广告映射
目标
把杂乱的关键词集合转成可执行的分类、否定和投放结构，使探索范围与高转化目标同时可控。
适用任务
- 竞品反查后词太多、重复多、不知道如何下手。
- 需要为新品搭建自动、广泛、词组、精准和商品投放的职责分工。
- 广泛流量跑偏，需要建立词根级前置否定。
开始前要拿到
- 产品事实表：品名、材质、功能、兼容性、尺寸、人群、场景和明确不适用项。
- 授权来源的竞品关键词、Amazon 搜索词报告、品牌分析或其他可追溯数据。
- 搜索量或代理指标、CPC、转化、订单和自然排名数据。
缺失的数据要明确列为缺口，并把结论标成事实、估算或假设；不要补造数字。
不可妥协的边界
- 竞品词不等于本品词；每个关键词必须通过产品事实和相关性复核。
- 不得购买、抓取或使用无权访问的竞品机密广告数据。
- 否定词先检查歧义和变体，避免一次词根否定误伤有效查询。
- 当前 Amazon 官方政策、帮助页、账户资格和后台实际字段优先于本 Skill 中的经验框架；规则可能变化时先核验。
- 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。
- 不输出原素材的创作者身份、账号、链接、视频编号或可反查线索；当前业务证据的官方来源、采集时间和口径仍需保留。
第三方 MCP 数据
只有在本任务确实需要外部市场、竞品、关键词或公开网页证据时，才读取 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，不照搬历史工具名。
- 凭证只从环境变量读取，不放进命令参数、URL、Skill、结果文件或 Git。
- tools/call 可能计费。调用前展示 Provider、工具名、无密钥参数、预计成本与输出位置，核对已有授权覆盖后才加 --allow-cost；该标志不是费用上限。
- 第三方数据标为估算或代理证据，记录 Provider、工具、无密钥参数、查询时间和原始结果位置；失败一次后记录缺口，不反复消耗额度。
- 脱敏结果用 --output 写到 Skill 包之外的任务私有目录；不假设安装位置受仓库 .gitignore 保护，不把运行结果写入 Skill 包。
工作流
1. 合并与去重
统一大小写、单复数和常见拼写，保留原始来源字段，删除重复项但不丢失证据链。
2. 建立词根 taxonomy
至少区分高意图属性词根、覆盖型高频词根、通用词、品牌词、竞品词和不相关词根。
3. 评分排序
按事实相关性、购买意图、流量、竞争、预估转化和利润空间评分；缺失数据不伪造，降级为待验证。
4. 映射广告职责
自动用于受控发现，广泛或词组用于词根扩展，精准用于已验证词，商品投放用于相似详情页或类目机会。
5. 建立否定逻辑
明确哪些词做精准否定、哪些词根可做词组否定，并记录否定原因和复核人。
6. 持续迁移
按固定窗口把出单搜索词迁移、把高耗无转化词降级或否定，并同步检查广告间的重复覆盖。
判断标准
- 词库每行至少包含关键词、相关性、意图、词根、证据来源、建议投放和状态。
- 高转化只是预测时必须明确标为假设。
- 广告结构能回答每一组的探索对象、预算职责和迁移出口。
必须交付的结果
- 去重后的关键词主表。
- 词根分类、优先级和不相关词表。
- 关键词到广告类型的映射表。
- 迁移、否定和复核节奏。
结尾列出数据窗口、关键假设、证据缺口、风险和下一步；如包含待执行动作，单独放在“待批准变更”中。
What ships with it
3 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 · 85 lines · 84 tokens per session scan A abba7e77d985
sealeap-amazon-keyword-selection-and-campaign-mapping is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 84 tokens to every session and 1,200 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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