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-prime-day-planninggit 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-prime-day-planning)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-prime-day-planning"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-prime-day-planning/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-prime-day-planning"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-prime-day-planning.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 | $0.00132 | $0.00999 |
| Opus 5 | $0.00066 | $0.00500 |
| Sonnet 5 | $0.00026 | $0.00200 |
| Haiku 4.5 | $0.00013 | $0.00100 |
Grade A, and why
sealeap-amazon-prime-day-planning 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.
What it actually says
Amazon Prime Day 广告规划
目标
从 116 条带限定条件的历史洞察中找到“真正适用于当前问题的行”，保留 marketplace、广告产品、指标、广告主资格、品类、花费门槛和时间窗口，再将其降级为规划先验，用当前账户数据决定活动节奏与实验。
先读 references/source-and-guardrails.md。查询数据使用 scripts/filter_insights.py；输出计划参考 references/planning-workflow.md。
查询数据
查看摘要：
python3 scripts/filter_insights.py --summary
按站点、指标和广告产品筛选：
python3 scripts/filter_insights.py --marketplace DE --metric ROAS --ad-product "Sponsored Brands"
搜索限定条件：
python3 scripts/filter_insights.py --marketplace US --contains "brand owner" --format json
marketplace 会匹配行中列出的单站点/区域；All 不是任意行通配符，而是源数据明确标记的全球切片。
工作流
1. 锁定当前事件
确认年份、marketplace、官方 event dates、广告 profile、时区、币种、ASIN/品类、Deal/优惠资格、库存到仓时间和活动目标。不得沿用 2025 时间窗口作为当前日历。
2. 锁定经济性与库存
计算贡献毛利、盈亏平衡 ACOS、促销成本、库存覆盖天数、补货截止和断货风险。销量目标超出库存或现金流承受时先 HOLD_SCALE。
3. 筛选可比洞察
从最严格条件开始：单站点 > 区域 > All；同指标、同广告产品、相近广告主资格/品类/花费门槛。每个候选行完整保留 Content 中的限定说明。
如果没有足够可比行，输出 NO_COMPARABLE_SOURCE_SLICE，不要拿全球/其它品类数字填空。
4. 解释而非平均
- DPV、Units、Sales 的 uplift 是事件期对材料所列基准期的比较；
- ROAS 是材料所列事件期内的比率；
- 各行可能来自不同 marketplace、资格和广告产品，不得求简单平均或排名；
- 历史洞察只帮助设定问题和护栏，不直接设置预算或目标。
5. 形成三阶段计划
PREHEAT：验证零售准备、扩充合格搜索词/人群、建立基线；EVENT：把预算优先给当前账户已证明的高贡献对象，设置实时停止线；TAIL：收紧低效流量、继续合规再营销、等待归因成熟并复盘。
每阶段只给活动角色、证据和上下限；不照抄资料预算比例。
6. 设计一个实验
只改变一个变量，例如预算释放节奏、广告产品组合、关键词层或创意。固定其它主要条件，记录最大花费、库存护栏、成功/停止线和回退。
7. 审批与复读
所有 bid、budget、campaign 状态、target、Deal 或优惠变更必须列出逐对象旧值/新值并等待批准。写后复读当前值；请求成功不等于事件表现已改善。
必须交付
- 当前事件、账户、经济性和库存作用域；
- 选中的源行及全部 qualifier；
- 为什么可比、为什么其它行被拒绝；
- PREHEAT / EVENT / TAIL 草案；
- 一个单变量实验、审批和回退；
DRAFT / READY_FOR_REVIEW / HOLD状态。
What ships with it
5 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.
- 8d ago First seen · 83 lines · 132 tokens per session scan A 84aa4d4f95c0
sealeap-amazon-prime-day-planning is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 132 tokens to every session and 999 once invoked, about $0.0007 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 2026-09-04.
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