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-qinglong-amazon-ad-diagnostics-negative-targetinggit 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-qinglong-amazon-ad-diagnostics-negative-targeting)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-ad-diagnostics-negative-targeting"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-ad-diagnostics-negative-targeting/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-qinglong-amazon-ad-diagnostics-negative-targeting"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-ad-diagnostics-negative-targeting.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 | $0.00083 | $0.01178 |
| Opus 5.5 | $0.00033 | $0.00471 |
| Sonnet 5.5 | $0.00017 | $0.00236 |
| Haiku 4.5 | $0.00008 | $0.00118 |
Grade A, and why
sealeap-qinglong-amazon-ad-diagnostics-negative-targeting 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 广告诊断与实验：否定投放、搜索词分析、手动广告
目标
围绕否定投放、搜索词分析、手动广告，区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验。
使用范围
- 优先处理广告诊断与实验任务；从专项证据卡选择与当前对象和问题直接相关的主题。
- 集合名称用于维护文件归属，不限制用户要求的交叉验证。其他来源与业务域的证据须分别标注，再按同口径比较。
- 本 Skill 可独立使用，不依赖仓库中的私有语义稿。保留去标识化边界，不恢复原素材身份或逐条映射。
适用任务
- 诊断树
- 异常数据表
- 单变量实验卡
开始前要拿到
- 站点、账户与 ASIN/SKU 范围。
- 广告活动、广告组、投放和搜索词数据。
- 价格、库存、Featured Offer 与贡献毛利。
- 统一日期和归因窗口。
缺少字段时明确标为 UNKNOWN 或 NEEDS_EVIDENCE，不要补造数据。
不可妥协的边界
- 本 Skill 来自去标识化语义转译，不保留或推断素材来源身份，也不把素材观点冒充 Amazon 当前政策。
- 产品事实、账户事实和 Amazon 一方报告优先；第三方数据必须标明 Provider、站点、日期、样本和估算口径。
- 不捏造销量、搜索量、成本、合规状态、产品属性、评论、平台通知或执行结果。
- 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。
- 平台规则、费率、界面和资格会变化；执行前复核当前官方文档与后台状态。
工作流
- 锁定问题范围和基线，区分无曝光、低点击、低转化和利润问题。
- 在活动、投放、query 和广告位层拆解数据，避免只看总均值。
- 核对零售准备度、相关性、预算、竞价、库存和页面因素。
- 提出一个单变量实验，写明样本门槛、停止条件和回退值。
- 在观察窗结束后同时复核广告、自然销售和贡献利润。
专项路由
- 五点描述：按购买问题组织收益、事实和适用边界，避免重复或虚假承诺。
- 否定投放：以足够点击与明确不相关证据建立否定候选，保留审批和回退记录。
- A+ 页面：按购买决策顺序规划模块、证据、对比和 FAQ，并校验移动端。
- 搜索词分析：在 query 层聚合曝光、点击、订单和花费，再决定收词、否定或页面补强。
- 标题：依据实时类目规则和已证实产品事实设计可读标题，不堆词。
- 手动广告：明确关键词或商品投放对象、匹配方式、出价和否定关系。
- 自动广告：分拆自动投放子类型，提取有效搜索词并隔离浪费流量。
- 匹配方式：分别定义发现、验证和收割角色，避免不同匹配方式互相争抢。
完整的 27 张主题证据卡见 references/topic-cards.md；5 种原有组合见 references/scenario-patterns.md。
第三方数据（可选）
只有在用户数据或 Amazon 一方报告不足时，才按 references/mcp-data-plan.md 发现实时 schema、执行 dry-run，并在可能计费的调用前核对具体请求与预算授权；已有授权覆盖时不重复索取。凭证通过环境变量注入，结果保存到 Skill 包之外的任务私有目录。
必须交付的结果
- 诊断树
- 异常数据表
- 单变量实验卡
- 待批准变更清单
- 复盘与回退记录
结尾列出站点、时间窗、数据口径、证据、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
6 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 · 77 lines · 83 tokens per session scan A e7abc2ecc8ac
sealeap-qinglong-amazon-ad-diagnostics-negative-targeting is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,178 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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