Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting. Use when the user asks whether a new product can start with ASIN targeting, how to choose competitor ASINs, or when to move discovered queries into exact campaigns. Avoid review manipulation and ranking guarantees.
npx skills add https://github.com/xjli360/sealeap-amazon-skills --skill sealeap-amazon-asin-targeting-strategy
用少量高匹配商品目标验证详情页与搜索环境机会,并把被报告证实的查询或目标迁移到独立结构。
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。
按品类相关性、功能相似度、受众重合、需求、价格区间和本品真实优势筛选。
优先单目标或小同质组,关闭不必要的扩展,确保每个结果可归因。
根据广告位报告区分商品页面与搜索位置表现;调整基础竞价和位置系数前先计算有效出价。
记录点击、CVR、CPA、已购商品、搜索词和利润;无订单时先判断样本和相关性。
稳定商品目标独立管理;报告中出现且证据充分的高转化查询可迁移到精准关键词广告。
只有首批结果成立后才增加更广目标,并持续检查内部重复与库存。
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-asin-targeting-strategy 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.