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-conversational-shopping-discoverabilitygit 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-conversational-shopping-discoverability)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-conversational-shopping-discoverability"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-conversational-shopping-discoverability/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-conversational-shopping-discoverability"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-conversational-shopping-discoverability.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 | $0.00080 | $0.01102 |
| Opus 5 | $0.00040 | $0.00551 |
| Sonnet 5 | $0.00016 | $0.00220 |
| Haiku 4.5 | $0.00008 | $0.00110 |
Grade A, and why
sealeap-amazon-conversational-shopping-discoverability 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 对话式购物可发现性
目标
让商品事实和真实使用场景更完整、结构化、可验证，使搜索与购物助手更容易理解商品适用性。
适用任务
- Listing 属性不完整或场景表达薄弱。
- 准备适配 Rufus、Alexa 或其他对话式购物入口。
- 需要从真实客户问题和反馈中提炼场景内容。
开始前要拿到
- 产品规格、说明书、认证、兼容性、限制条件和真实场景证据。
- Listing 当前标题、五点、属性、图片、A+、视频和本地化版本。
- 真实客户搜索词、客服问题、退货原因和评论主题聚合。
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
- 不得安排、购买或撰写买家评论与 Q&A，也不得把营销话术伪装成客户内容。
- 所有场景、性能和兼容性声明必须有产品事实或证据支持。
- 不承诺被任何购物助手推荐；推荐机制和展示会变化，需核对当前官方信息。
- 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。
第三方 MCP 数据
只有在本任务确实需要外部市场、竞品、关键词或公开网页证据时，才读取 references/mcp-data-plan.md，并使用 scripts/mcp_research.py。
- 先动态执行 tools/list、search-tools 和 describe，依据实时 inputSchema 构造参数，不照搬历史工具名。
- 凭证只从环境变量读取，不放进命令参数、URL、Skill、结果文件或 Git。
- tools/call 可能计费。调用前展示 Provider、工具名、无密钥参数、预计成本与输出位置，核对已有授权覆盖后才加 --allow-cost；该标志不是费用上限。
- 第三方数据标为估算或代理证据，记录 Provider、工具、无密钥参数、查询时间和原始结果位置；失败一次后记录缺口，不反复消耗额度。
- 脱敏结果用 --output 写到 Skill 包之外的任务私有目录；不假设安装位置受仓库 .gitignore 保护，不把运行结果写入 Skill 包。
工作流
1. 建立事实底稿
整理结构化规格、兼容性、使用限制、认证和证据来源，先解决内部矛盾。
2. 补全属性
填写当前类目允许且适用的属性，不用关键词堆砌替代字段含义。
3. 映射真实场景
从搜索词、客服、退货和评论主题聚合出高频任务，用产品事实判断支持、不支持或需条件支持。
4. 改造内容
在标题、五点、图片、A+ 和视频中自然表达关键场景、限制和证明，保持可读性并做本地化。
5. 处理客户内容边界
只观察真实客户评论和问题的主题；品牌回答保持事实中立，不诱导评价或虚构提问。
6. 验证效果
用真实对话式查询做发现性检查，并跟踪会话、转化和退货变化；把结果标为相关观察。
判断标准
- 每条卖点和场景能回到事实证据。
- 属性、图片和文案之间一致。
- 不包含任何评论或 Q&A 植入动作。
必须交付的结果
- 产品事实与场景矩阵。
- 属性缺口清单。
- Listing 内容改写草稿。
- 对话式查询测试与监控方案。
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
3 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.
- 5d ago First seen · 85 lines · 80 tokens per session scan A 2b79e8cd51ee
sealeap-amazon-conversational-shopping-discoverability is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 80 tokens to every session and 1,102 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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