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-eu-amc-audiencegit 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-eu-amc-audience)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-eu-amc-audience"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-eu-amc-audience/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-eu-amc-audience"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-eu-amc-audience.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00174 | $0.01618 |
| Opus 5 | $0.00087 | $0.00809 |
| Sonnet 5 | $0.00035 | $0.00324 |
| Haiku 4.5 | $0.00017 | $0.00162 |
Grade A, and why
sealeap-amazon-eu-amc-audience 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SeaLeap Amazon EU/旺季 AMC 受众与衡量
把欧洲站的增长问题转换为可审计的 AMC 分析、受众定义、激活草案和衡量方案。先确定业务问题和数据资格，再选择模型；不要先创建人群包，再寻找解释。
强制边界
- 把 AMC 定义为隐私安全的数据净室/分析与受众层，不把它说成普通广告报表、用户级 CDP 或直接投放系统。
- 只使用聚合、匿名化或假名化信号。不得尝试导出、重识别、拼接或推断个人身份；不得规避最小受众量、隐私阈值或查询抑制。
- 把源材料中的模型数量、受众窗口、触达频次、运行时长、案例成果和账户截图标为
TRAINING_CASE，不当作当前账户事实或官方通用门槛。 - 在使用前核对当前 AMC 实例、marketplace、广告主、可用表、lookback、受众资格、激活渠道、延迟和官方政策。欧洲各站不要无依据合并。
- 将 advertiser/profile/store/AMC instance ID 视为业务对象，不视为授权。只在服务端已验证的主体和站点范围内读取或写入。
- 默认只输出分析与草案。创建查询、保存受众、调整 audience bid、预算、竞价或状态前，逐项等待人工确认。
- 一张实验卡只改变一个主变量。路径、重叠和相关性不能单独证明增量或因果。
阅读 references/source-and-guardrails.md 获取完整信源、证据等级和内容限制。处理旺季与无代码受众时再读 references/peak-audience-course.md。
工作流
1. 锁定问题、主体与站点
记录业务目标、国家站点、广告主、AMC instance、Ads profile、品牌/ASIN、广告类型、时间窗和负责人。把问题写成可回答句，例如：
- 哪些广告组合触达后更可能产生新客，而不是“哪个广告最好”；
- 旺季曝光未购买人群是否值得淡季再营销；
- 高客单产品从首次触达到购买需要多久、多少次触达；
- SP/SB/SD/DSP 的路径和重叠是否支持预算或频次假设。
2. 通过就绪门
检查实例与权限、站点和时区、数据覆盖、广告活动映射、转化定义、归因/回看窗口、隐私阈值、受众可激活性、库存和退货后利润。使用 references/readiness-and-europe.md。
缺少关键条件时输出 HOLD / NEEDS_DATA；不要把 No results returned 自动解释为零，也不要默认数据在其他页面必然存在。
3. 选择最小分析模型
按问题只选必要模型：
| 业务问题 | 首选分析 | 主要输出 |
|---|---|---|
| 决策周期 | Time to Conversion | 转化耗时分布与对比 |
| 渠道先后关系 | Path to Conversion by Campaign Groups | 路径、触点顺序、辅助触达 |
| 重复覆盖 | Ad-type overlap / reach-frequency | 独占、重叠、频次与浪费假设 |
| 拉新 | New-to-brand | 新客购买/销售占比与路径 |
| 旺季长尾 | Seasonal off-peak exposure | 旺季曝光未转化候选受众 |
| 人群扩展 | Rule-based / lookalike | 精准规则或相似拓展草案 |
阅读 references/analysis-models.md 获取指标定义、对比原则和误读防护。
课程所示无代码模板名和数量可能变化。当前实例不可见时，保留业务定义并标 TEMPLATE_NOT_VERIFIED，不要虚构模板或 SQL。
4. 定义受众而非复制案例
用“纳入条件 + 排除条件 + 时间窗 + marketplace + 预估规模 + 用途 + 到期日”定义受众。根据问题选择规则型或相似型；不要把拼图桌案例中的 30/60/90 天窗口套给所有产品。
阅读 references/audience-playbooks.md 获取潮汐人群、探索者、痛点/场景、竞品关注者和全漏斗分层方法。
5. 生成激活草案
明确激活位置是 SP、SB、SD 还是 DSP，以及 include、exclude、竞价加成、再营销或相似拓展中的哪一个。先确认当前控制台支持该受众与操作，再创建单变量动作卡。
What ships with it
8 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.
- agents/openai.yaml 300 B
- references/activation-and-measurement.md 1.9 KB
- references/analysis-models.md 2.5 KB
- references/audience-playbooks.md 2.7 KB
- references/output-contract.md 2.3 KB
- references/peak-audience-course.md 2.3 KB
- references/readiness-and-europe.md 2.1 KB
- references/source-and-guardrails.md 3.5 KB
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.
- 8d ago Changed · +2 lines · -13 tokens per session 3949247a3679
- 12d ago First seen · 86 lines · 187 tokens per session scan A 847131f580c1
sealeap-amazon-eu-amc-audience is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 5d ago), licensed MIT. It adds 174 tokens to every session and 1,618 once invoked, about $0.0009 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 2026-08-30.
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