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-acos-diagnosticsgit 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-acos-diagnostics)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-acos-diagnostics"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-acos-diagnostics/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-acos-diagnostics"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-acos-diagnostics.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 | $0.00148 | $0.02411 |
| Opus 5 | $0.00074 | $0.01205 |
| Sonnet 5 | $0.00030 | $0.00482 |
| Haiku 4.5 | $0.00015 | $0.00241 |
Grade A, and why
sealeap-amazon-acos-diagnostics 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Ads ACOS 核心指标诊断
目标
把“ACOS 高不高”改写成一条可复核的问题链：先确认策略目标和经济性，再统一数据口径，重算 ACOS 及其驱动项，定位 CPC、CVR、客单价或流量结构断点，最后只提出一个可归因的实验。
先读 references/source-and-guardrails.md。需要公式与指标口径时读 references/metric-system.md，需要完整诊断树时读 references/diagnostic-tree.md。课程案例只能结合 references/case-study-and-caveats.md 使用；需要 Benchmark 算法与误读防护时读 references/benchmark-methodology.md。
核心原则
ACOS = 广告花费 ÷ 广告归因销售额 × 100%，也可在同一口径下拆为CPC ÷ (CVR × 广告订单客单价) × 100%。- ACOS 是结果指标，不是所有广告目标的唯一评价标准。测试、守位、品牌获客、推排名和成熟品利润的主目标不同。
- 盈亏判断使用“扣除商品成本、平台费用、履约、折扣、退货等广告外可变成本后的贡献毛利率”。缺少完整成本时不得把普通毛利率写成精确盈亏线。
- 广告归因销售额与总销售额不得混用；ACOS、TACOS、ROAS 必须分别命名。
- 不跨 marketplace、profile、币种、广告类型、归因窗口、日期或层级直接拼接。
- Benchmark 是同业参照，不是目标或因果解释。当前可用性、同业组、分位数与指标定义必须在控制台/API 重新确认。
- 默认只读。任何 bid、budget、placement、target、否定词、状态或结构变更均需逐对象人工批准。
先声明模式
DIAGNOSE：重算和定位，不生成变更；默认；DRAFT：生成单变量实验草案；RELEASE_PREP：生成审批卡、旧值/新值、停止线和回退；APPROVED_WRITE：只执行用户本轮明确批准的一个动作，写后复读。
核心工作流
1. 先问“这轮广告要完成什么”
只选一个主目标：
| 目标 | 首要判断 | ACOS 的位置 |
|---|---|---|
| 测试商品/查询 | 相关性与有效样本 | 护栏，不是首要结果 |
| 防守 | 关键流量是否守住且经济可承受 | 与覆盖、份额、利润并看 |
| 品牌获客 | 品牌新客与后续价值 | 与新客成本、店铺行为并看 |
| 推排名 | 排名/自然贡献是否改善 | 与总利润、TACOS、库存并看 |
| 成熟品利润 | 贡献利润和现金效率 | 关键结果之一 |
若用户只说“把 ACOS 降低”，先确认降低 ACOS 是否会伤害本轮主目标。证据不足时仍可继续只读诊断，但把目标标为 NEEDS_DATA。
2. 锁定分析作用域
记录：
- 已验证的 seller、marketplace、广告 profile 和授权范围；
- 日期、时区、币种、广告类型、归因窗口、数据更新时间；
- 分析层级：portfolio / campaign / ad group / target / search term / placement / advertised ASIN；
- 当前价格、优惠、库存、Featured Offer、评分、配送和 Listing 变更；
- 单位经济：售价、折扣、COGS、FBA/佣金/履约、退货/退款、其它可变成本。
不要用账户汇总 ACOS 直接解释某个词，也不要用某个词的 CTR 替代 campaign 目标表现。
3. 先重算，不信任表格中的派生值
准备 JSON 后运行：
python3 scripts/acos_diagnose.py --input references/acos-input.example.json
至少提供 impressions / clicks / spend / orders / ad_sales。脚本会计算 CTR、CPC、CPM、CVR、AOV、CPA、ACOS、ROAS，以及可选 TACOS，并对上报指标做一致性检查。
What ships with it
9 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 321 B
- references/acos-input.example.json 633 B
- references/benchmark-methodology.md 1.6 KB
- references/case-study-and-caveats.md 1.8 KB
- references/diagnostic-tree.md 3.1 KB
- references/metric-system.md 2.5 KB
- references/output-contract.md 1.9 KB
- references/source-and-guardrails.md 2.4 KB
- scripts/acos_diagnose.py 6.7 KB runs code
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 d16a9ae06e65
- 12d ago First seen · 169 lines · 148 tokens per session scan A 1ffc3d706d90
sealeap-amazon-acos-diagnostics is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 148 tokens to every session and 2,411 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 2026-08-30.
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