Plan an ad-intensive but policy-compliant Amazon launch that expands indexed and converting keyword coverage while preserving profitability and inventory guardrails. Use when the user asks for a pure white-hat launch, broad keyword coverage, when to move terms into exact campaigns, or how to combine auto, broad, product targeting, and eligible promotions. No review manipulation or ranking guarantees.
npx skills add https://github.com/xjli360/sealeap-amazon-skills --skill sealeap-amazon-white-hat-product-ranking
通过分层广告和真实转化扩大有效关键词覆盖,把主要出单词变成可独立管理的资产,并在成熟后收缩无效花费。
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。
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。
检查商品信息、价格、库存、配送、合规和真实评价基础,未达标时先修 Listing 或产品。
建立受控自动、相关词根广泛和高相似商品投放,分别定义探索对象和预算。
首轮不直接无差别冲头部词,优先覆盖中部词和高意图词根;不相关词根提前审慎否定。
当某词达到相关性、样本和利润门槛且在探索层预算不稳时,迁移到独立精准广告。
随着稳定词增多,再逐步扩展头部或更泛流量;符合资格时用官方促销做独立实验,避免同时改变过多变量。
按增量利润保留主力词,降低重复探索和低效广告,并持续监控库存与自然侧。
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。
Take xjli360/sealeap-amazon-white-hat-product-ranking from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.