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-ad-traffic-allocationgit 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-ad-traffic-allocation)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ad-traffic-allocation"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ad-traffic-allocation/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-ad-traffic-allocation"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-ad-traffic-allocation.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 | $0.00081 | $0.00804 |
| Opus 5 | $0.00041 | $0.00402 |
| Sonnet 5 | $0.00016 | $0.00161 |
| Haiku 4.5 | $0.00008 | $0.00080 |
Grade A, and why
sealeap-amazon-ad-traffic-allocation 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 广告流量分配
目标
用“发现高转化组合”和“重分配预算”两个循环管理广告，避免同时改变关键词、位置和预算导致无法归因。
适用任务
- 解释广告结构中探索活动与主力活动的职责。
- 诊断同一关键词在不同广告位表现差异。
- 在预算固定时放大赢家、减少输家。
开始前要拿到
- 查询、目标、广告活动和广告位级表现。
- 商品价格、利润、阶段目标和库存状态。
- 已知的关键词相关性与商品投放相似度。
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
- 广告位报告不等于可精确控制的自然页码或固定展示位置。
- 不可在样本不足时把预算从一个随机赢家全部迁走。
- 广告优化不能绕过商品页承接力、价格和库存问题。
- 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。
工作流
1. 拆分两个循环
探索循环负责找词、商品目标和广告位；利用循环负责给已验证组合稳定预算。
2. 隔离变量
先固定目标测试广告位，或固定广告位策略测试目标，避免多变量一起变化。
3. 定义赢家
以相关性、CVR、CPA、贡献利润和样本强度联合判定，而不是只看订单或 ACoS。
4. 迁移预算
小步增加赢家预算或竞价，减少低相关和持续亏损流量；每次保留前后对照。
5. 持续再探索
保留受控探索预算，防止主力词老化或流量结构变化后失去新机会。
6. 监控总盘
观察 TACoS、总贡献利润、自然订单、库存和广告间蚕食。
判断标准
- 每个建议指出它属于探索还是利用。
- 预算迁移有最小样本和最大调整幅度。
- 报告同时呈现局部广告指标和总业务结果。
必须交付的结果
- 探索与利用广告地图。
- 关键词加广告位组合的绩效表。
- 预算增加、减少和继续观察清单。
- 下一轮单变量实验。
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
1 file 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 · 75 lines · 81 tokens per session scan A 1c0afb3c5e3c
sealeap-amazon-ad-traffic-allocation is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 81 tokens to every session and 804 once invoked, about $0.0004 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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