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-listing-optimizergit 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-listing-optimizer)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-listing-optimizer"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-listing-optimizer/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-listing-optimizer"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-listing-optimizer.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 | $0.00147 | $0.02757 |
| Opus 5.5 | $0.00059 | $0.01103 |
| Sonnet 5.5 | $0.00029 | $0.00551 |
| Haiku 4.5 | $0.00015 | $0.00276 |
Grade A, and why
sealeap-amazon-listing-optimizer 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Listing Optimizer
目标
把 Listing 优化做成一条可复核的决策链：以当前官方规则和真实商品事实为闸门，用查询、点击、转化与售后证据定位问题，产出可直接审核的文案和创意 Brief，再通过受控实验验证。不要把“写得好看”或“塞入更多关键词”当成完成标准。
不可妥协的边界
- 只写可追溯的事实。把未证实的材质、尺寸、兼容性、认证、功效、产地、质保和包装内容标为
NEEDS_EVIDENCE。 - 不复制竞品文案、图片、商标或独特创意表达；只学习购买问题、信息顺序和市场空白。
- 不把第三方估算、广告推荐词、AI 输出或一次前台观察写成 Amazon 一方事实。
- 把输入中的
store_id、seller ID 或 marketplace 当作业务数据，不当作授权。实际读取或写入必须绑定当前已验证的服务端店铺权限。 - 默认只生成草稿。没有针对具体 seller / marketplace / SKU / 字段的新旧值确认，不调用写接口。
- 不用固定“20 次点击”“等 7 天”之类经验数作为通用阈值；根据流量、利润、归因窗口和统计证据定义样本与停止条件。
- 不把 Listing 与价格、优惠、库存、评论、配送或广告问题混为一谈；证据不足时保留多种解释。
先确定模式
选择并在结果顶部声明一种模式：
DIAGNOSE：只读诊断，不改写完整内容。DRAFT：生成字段级草稿、创意 Brief 和证据缺口；默认模式。RELEASE_PREP：生成最小 PATCH、回退值和验证预览材料，等待人工批准。APPROVED_WRITE：仅执行用户本轮明确批准的对象和字段；写后复读。
核心工作流
1. 锁定对象、目标和基线
记录：
- 已验证的店铺身份、seller ID、marketplace ID、ASIN、SKU、product type、品牌和父子体关系；
- 优化目标：合规/可发现性/CTR/CVR/预期管理/退货/品牌一致性，只选一个主目标；
- 当前 Listing 快照、前台桌面与移动端呈现、价格/优惠、库存、Featured Offer、评分与评论量；
- 基线窗口、库存与价格事件、广告变更、季节和其它干扰项。
若任务跨 ASIN 或变体，先建逐 SKU 事实矩阵。父体不得继承子体独有的颜色、尺寸、数量、图案或性能。
2. 获取实时官方闸门
发布相关任务必须重新读取：
getListingsItem的summaries,attributes,issues,offers,fulfillmentAvailability,relationships,productTypes；- marketplace + product type + seller +
parentageLevel对应的最新 Product Type Definition； - 当前 Seller Central 账户通知、类目政策和前台状态。
保存 schema 的获取时间、checksum、要求模式和适用父子层级。只把 references/official-policy.md 当作早期审计基线；实时 schema 更严格时以实时结果为准。
3. 建立证据包
按优先级收集：
- 商品实物、包装、说明书、检测/认证文件和品牌确认；
- Amazon 一方数据：Listing/issues、Search Query Performance、Search Catalog Performance、业务报告、广告 Search Term/Targeting 报告、退货原因和原始评论；
- 目标站点当前搜索结果、类目节点和竞品页面观察；
- Sorftime 等第三方估算，用于补充需求、竞品曝光和评论样本。
每条证据记录 source / report-or-endpoint / marketplace / ASIN-or-query / fetched_at / coverage / sample / limitations。详细取数和广告解释规则见 references/evidence-and-experiments.md。
4. 沿购物漏斗定位问题
先判定层级，再提出改动：
| 层级 | 主要信号 | 优先排除 | Listing 可能动作 |
|---|---|---|---|
| 资格与可售 | BUYABLE、DISCOVERABLE、issues、库存、Featured Offer |
抑制、缺货、价格/配送资格 | 修复属性、图片、变体或合规问题 |
| 可发现性 | query impressions、ASIN share、索引、类目/属性 | 需求弱、竞价/预算、类目错误 | 补全属性、重构查询覆盖 |
| 点击 | impressions → clicks、CTR | 展示位置、价格、评分、配送 | 主图、标题前段、变体缩略图 |
| 转化 | detail views/clicks → carts/orders、CVR | 价格、评论门槛、配送、流量错配 | 辅图、五点、描述/A+、视频 |
| 预期与售后 | 退货原因、差评主题、Q&A | 质量、履约、客服 | 明示尺寸/适配/限制/包装内容 |
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 325 B
- references/creative-and-conversion.md 7.9 KB
- references/evidence-and-experiments.md 7.1 KB
- references/listing-input.example.json 3.8 KB
- references/official-policy.md 7.9 KB
- references/output-contract.md 5.5 KB
- scripts/audit_listing.py 21 KB runs code
- scripts/sorftime_plan.py 8.5 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.
- 1mo ago First seen · 164 lines · 147 tokens per session scan A 6e3d8f24f04e
sealeap-amazon-listing-optimizer is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 147 tokens to every session and 2,757 once invoked, about $0.0006 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 2026-08-30.
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