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-new-product-conversion-readinessgit 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-new-product-conversion-readiness)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-new-product-conversion-readiness"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-new-product-conversion-readiness/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-new-product-conversion-readiness"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-new-product-conversion-readiness.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 | $0.00085 | $0.01111 |
| Opus 5.5 | $0.00034 | $0.00444 |
| Sonnet 5.5 | $0.00017 | $0.00222 |
| Haiku 4.5 | $0.00009 | $0.00111 |
Grade A, and why
sealeap-amazon-new-product-conversion-readiness 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.
What it actually says
Amazon 新品转化就绪诊断
目标
找出新品推不动是可售、点击、转化、流量还是经济性问题，并先修承接短板再扩大流量。
适用任务
- 新品有曝光或点击但没有订单。
- 团队把评论数量当作唯一启动条件。
- 需要建立商品页与广告的联合排查顺序。
开始前要拿到
- 可售状态、库存、Buy Box、价格、优惠和配送承诺。
- 主图、标题、五点、A+、视频、属性、合规评价和退货反馈。
- 查询级曝光、CTR、CVR、CPA 与竞品可比数据。
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
- 拒绝虚假订单、免评单、测评、付费好评、评论合并和人为操纵参考价。
- 评论数量少不等于必须暂停推广；用真实转化障碍判断。
- 不得把广告带来的短期变化宣称为平台认可或排名因果。
- 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. 检查可售漏斗
从抑制、库存、配送、Featured Offer 和价格开始，先排除根本无法顺畅下单的问题。
2. 诊断点击
对比同一查询环境下的主图、价格、优惠、评分和标题相关性，找出 CTR 短板。
3. 诊断转化
检查内容证据、功能表达、变体选择、风险消除、真实评价主题和售后信息。
4. 审查流量
确认广告查询与商品事实匹配，集中预算到高意图主题，否定明确无关流量。
5. 建立合规反馈基础
使用符合资格的 Vine、Request a Review 和中立售后；把差评主题转为产品或说明改进。
6. 设置验证门槛
每个改动设前后窗口、目标指标和止损，承接仍弱时回到产品决策而不是继续烧钱。
判断标准
- 漏斗各层使用对应指标，不用评论数解释所有问题。
- 商品页改动有事实依据，不制造虚假稀缺、折扣或证明。
- 广告预算只在承接达标后扩大。
必须交付的结果
- 新品漏斗诊断。
- 商品页优先修复清单。
- 合规评价与售后计划。
- 精准流量验证方案和停止条件。
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.
- 1mo ago First seen · 85 lines · 85 tokens per session scan A 88bc361f9eb0
sealeap-amazon-new-product-conversion-readiness is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 1,111 once invoked, about $0.0003 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 2026-09-07.
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