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Amazon Keyword Research

这是一款亚马逊关键词研究工具,核心功能含种子词拓展、反向ASIN提取、聚类、竞争难度评估等。支持输入种子词、竞品ASIN、产品描述等,针对多目标市场生成关键词聚类、优先级矩阵、投放放置地图、否定词表等结果,适用于卖家优化Listing SEO、制定PPC策略,还可保存历史关键词集。

mguozhen
mguozhen
热度 58.21
最近更新 2026-05-11 17:34:36
分类:营销依赖:代码执行 / 第三方 API应用场景:市场分析 / 报告撰写 / 文案创作

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add mguozhen-amazon-keyword-research
Metadata
name
amazon-keyword-research
description
Amazon keyword research and strategy agent. Input a seed keyword, product idea, or competitor ASIN — get keyword clusters, search volume estimates, competition level, and a prioritized keyword strategy for your listing and PPC campaigns. Triggers: keyword research, amazon keywords, keyword strategy, search volume, amazon seo, listing keywords, ppc keywords, keyword discovery, long tail keywords, keyword clusters, amazon keyword tool, keyword difficulty, search term research, asin keywords, amazon search ranking
allowed-tools
Bash
metadata
{"openclaw":{"homepage":"https://github.com/mguozhen/amazon-keyword-research"}}

Amazon Keyword Research Agent

Build a complete keyword strategy for any Amazon product. From a seed keyword or competitor ASIN, discover high-converting terms, estimate volumes, and prioritize for listing SEO and PPC.

Commands

kw research [keyword]           # full keyword research from seed term
kw asin [ASIN]                  # reverse ASIN keyword extraction
kw cluster [keyword]            # build keyword clusters/themes
kw difficulty [keyword]         # assess competition level
kw trending                     # find trending search terms
kw negative [keyword]           # generate negative keyword list
kw listing [keyword]            # extract keywords for listing placement
kw save [name]                  # save keyword set to memory
kw history                      # show saved keyword sets

What Data to Provide

  • Seed keyword — your main product keyword (e.g., "yoga mat")
  • Product description — helps discover related terms
  • Competitor ASINs — reverse-engineer their keyword strategy
  • Target market — US, UK, DE, etc.
  • Budget/focus — broad discovery vs. focused exact-match targeting

Keyword Research Framework

Phase 1: Seed Expansion

  • Head terms → broad variations → long-tail
  • Synonym discovery (material, use case, audience, benefit)
  • Negative intent filtering (irrelevant, competitor brand)

Phase 2: Classification

Type Example Best Use
Head term yoga mat Title, high competition
Long-tail non-slip yoga mat 6mm thick Bullet points, backend
Use-case yoga mat for hot yoga Backend, A+ content
Audience yoga mat for beginners PPC exact match
Attribute extra thick yoga mat purple Long-tail PPC
Problem yoga mat without smell FAQ, backend

Phase 3: Priority Scoring

Score each keyword 1–10 based on:

  • Search volume (estimated from category context)
  • Relevance to product (1–5 scale)
  • Competition level (# of sponsored results)
  • Conversion intent (informational vs. transactional)

Priority formula: (Volume × Relevance × Intent) / Competition

Phase 4: Placement Strategy

Location Keyword Type Character Limit
Title Top 3–5 keywords 200 bytes
Bullet 1 Feature keywords 500 bytes each
Search Terms (backend) Long-tail, synonyms 250 bytes
A+ Content Audience/use-case No limit
PPC Broad Discovery terms —
PPC Exact Proven converters —

Output Format

  1. Keyword Universe — 30–50 keywords organized by cluster
  2. Priority Matrix — top 10 keywords to focus on first
  3. Placement Map — where each keyword goes (title/bullets/backend/PPC)
  4. Negative List — irrelevant terms to exclude from PPC
  5. Content Gaps — search intents your listing currently misses

Rules

  1. Never recommend stuffing keywords unnaturally into listing copy
  2. Flag brand name keywords (trademark risk)
  3. Prioritize buyer-intent keywords over informational
  4. Always separate PPC keywords from organic/listing keywords
  5. Recommend testing 2–3 title variations for new products