Amazon Opportunity Screener

亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+ 项商业维度（市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等）反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse search, niche metrics filter, low-competition niche, blue ocean niche, demographic-based selection, pain-point niche, price tier opportunity, sweet spot pricing, brand fragmentation时触发此技能。即使用户未明确说"反向选品"，只要其需求是按商业维度筛选符合条件的亚马逊赛道，也应触发此技能。

Amazon Opportunity Screener by Metrics

This skill guides you on how to reverse-search Amazon niches and keywords from a metrics pool aggregated from historical opportunity reports, helping sellers turn vague selection ideas (low competition, growing demand, blue ocean, pain-point opportunity, etc.) into concrete niche candidates.

Core Concepts

This tool exposes a queryable pool of niche-level metrics (~37 fields per record) distilled from past Amazon opportunity reports. Instead of generating a fresh report (forward analysis), it lets you reverse-filter the existing pool by 30+ business dimensions and returns matching (marketplace, keyword) records ranked by collection time (most recent first).

Records are at the niche / keyword level, not ASIN level. Each record represents a niche snapshot — its market size, growth, competition, price tiers, demographics, top features, and review themes.

Forward vs. reverse: Use linkfox-amazon-opportunity-report when the user has a keyword and wants a comprehensive AI report. Use this skill when the user has business criteria (filters) and wants to discover which keywords / niches fit.

Filter Dimensions

Filters are grouped into six business dimensions. All filter parameters are optional, but at least one of keyword / nicheName or any metric filter must be provided — fully empty calls are rejected.

Dimension	Example Parameters	Typical User Intent
Market size & growth	nicheRevenue360dMinUsdAtLeastGte, nichePeakSearchVolumeAtLeastGte, nicheSearchVolumeYoyChangePctAtLeastGte, nichePeakMonthGte/Lte	"Big enough market", "fast-growing", "Q4 seasonal"
Competition density	nicheBrandCountLte, nicheBrandCountYoyChangePctAtLeastLte, nicheTop5ProductClickSharePctAtLeastLte, featureTop5BrandSharePctAtLeastLte	"Newcomer-friendly", "brands fragmented", "no oligopoly", "brands exiting"
Price & tier	priceMinUsdGte, priceMaxUsdLte, priceSweetSpotMinUsdGte/Lte, priceEntryClickSharePctAtLeastGte, priceMidClickSharePctAtLeastLte, priceHighClickSharePctAtLeastGte	"Affordable focus", "premium-friendly", "mid-tier blue ocean"
Demographics	demoPrimaryAgeMinGte, demoPrimaryAgeMaxLte, demoGenderDominant, demoPrimaryIncomeTier, demoLifeStageTagsContains	"Female-driven", "high-income", "parents", "fitness enthusiasts"
Product features	featureNewAvgReviewCountAtLeastLte, featureEstablishedAvgReviewCountAtLeastLte, featureEmergingTrendTagsContains, featureUncommonFeatureTagsContains, searchTopCategory1Label	"New-product entry barrier low", "emerging trend", "uncommon feature edge", "set/kit niches"
Review insights	reviewPositiveTop1Topic, reviewPositiveTop1PctAtLeastGte/Lte, reviewNegativeTop1Topic, reviewNegativeTop1PctAtLeastGte/Lte, reviewNegativeTop2Topic, reviewStrategicInsightTagsContains	"Pain-point niche", "comfort-driven sellers", "size-issue opportunity"

See references/api.md for the full parameter list, types, value ranges, and response field map.

Supported Marketplaces

Currently only US (United States) is supported. Always set amazonDomain to US (or omit). If a user requests other marketplaces, inform them this tool currently only covers the US market.

调用方式

• API 端点：POST /amazon/opportunity/searchByMetrics（完整参数/响应/错误码见 references/api.md）
• Python 脚本：python scripts/amazon_opportunity_screener.py '<JSON 参数>' [--inline]
• 成本约束：本工具会消耗积分；同一会话同一参数组合默认只调用一次，脚本带 24h 本地缓存。失败/空结果不得自动换关键词、翻页或改邮编连续试探；需要继续检索时先向用户说明会产生额外消耗。

输出策略（脚本默认行为）：

• 始终将完整响应写入 <cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-amazon-opportunity-search-by-metrics-<timestamp>.json（<cwd> 为脚本执行时的工作目录，在 Claude Code 里即当前项目目录；<session> 取自环境变量 SESSION_ID，按用户任务自动聚合；禁止写入 /tmp，当前目录不可写则报错）
• 响应体 ≤ 8 KB：落盘后把完整 JSON 打印到 stdout
• 响应体 > 8 KB：落盘后 stdout 只输出摘要（顶层字段、常见计数如 total/costToken、最大列表字段的长度 + 前 3 条样本）
• 加 --inline 强制全量打印到 stdout（同样落盘）

读数据建议：先看摘要判断是否足够；需要具体字段时优先用 jq或ConvertFrom-Json 从保存的 json 文件按需抽取，避免整份 JSON 进入上下文。

How to Build Queries

The user expresses business intent in natural language; you map it to the smallest viable set of filters. Principles:

1. Convert intent into specific bounds: "low competition" → nicheBrandCountLte: 20; "fast-growing" → nicheSearchVolumeYoyChangePctAtLeastGte: 100 (≥100% YoY); "newcomer-friendly" → featureNewAvgReviewCountAtLeastLte: 500.
2. Start narrow, then loosen: First call usually with 2–4 strong filters and limit=25. If the result set is empty or too small, drop or widen the most aggressive filter rather than adding new ones.
3. Pair complementary signals: Brand-level + product-level concentration (featureTop5BrandSharePctAtLeastLte + nicheTop5ProductClickSharePctAtLeastGte) reveals "brands fragmented but products concentrated" — a brand-extension entry signal.
4. Snake_case fragments for tag fields: featureEmergingTrendTagsContains, demoLifeStageTagsContains, reviewNegativeTop1Topic, etc. accept snake_case word fragments and use LIKE matching. Pass a root word (size, parent, cordless) to cover normalized variants.
5. Faithful to user intent: Don't silently add filters the user didn't ask for. If they only said "growing", just filter on growth — don't also constrain price unless they mentioned it.

Common Scenarios

1. Niche reverse-lookup by keyword

json

{"keyword": "whoop band", "limit": 25}

2. Newcomer-friendly low-competition niches

json

{"nicheBrandCountLte": 20, "featureNewAvgReviewCountAtLeastLte": 500, "limit": 25}

3. High-growth blue ocean (≥100% YoY, brands not yet flooding in)

json

{"nicheSearchVolumeYoyChangePctAtLeastGte": 100, "nicheBrandCountYoyChangePctAtLeastLte": 30, "limit": 25}

4. Mid-tier price gap (low-price dominates, mid-tier scarce)

json

{"priceEntryClickSharePctAtLeastGte": 70, "priceMidClickSharePctAtLeastLte": 5, "limit": 25}

5. Pain-point entry — strong size complaints

json

{"reviewNegativeTop1Topic": "size", "reviewNegativeTop1PctAtLeastGte": 70, "limit": 25}

6. Premium-friendly female-driven niches

json

{"demoGenderDominant": "female", "demoPrimaryIncomeTier": "high", "priceHighClickSharePctAtLeastGte": 25, "limit": 25}

7. Q4 seasonal niches with ≥100k peak search

json

{"nichePeakMonthGte": 11, "nichePeakMonthLte": 12, "nichePeakSearchVolumeAtLeastGte": 100000, "limit": 25}

8. Track niches around a known competitor brand

json

{"featureTopBrandsContains": "WHOOP", "limit": 50}

Display Rules

1. Present data only: Render the returned niches as a clean comparison table — niche name / keyword, market size, growth, brand count, price range, key tags. No subjective business advice.
2. Surface the active filters: Echo the filter set you used so the user can adjust ("当前筛选：品牌数 ≤ 20 且搜索量同比 ≥ 100%").
3. Time-snapshot reminder: Records reflect data at collection time and are not continuously updated. Mention this when results look stale or contradict a user's external knowledge.
4. Empty / few-result handling: If data is empty or very short, suggest widening the most aggressive filter rather than re-asking the user from scratch.
5. Error handling: When a query fails, explain the reason based on the msg field (most often the "fully empty parameters" guard) and suggest adding at least one filter.
6. No secondary aggregation: The results power frontend rendering and are not stored, so they cannot be fed into @智能数据查询 (intelligent data query) for further aggregation. If users ask for grouped statistics across niches, do the calculation locally or pull a wider limit first.

Important Limitations

• US only: Currently only supports the United States marketplace (amazonDomain = US).
• No pagination: There is no page parameter. Increase limit (max 200) to widen the candidate pool; results are sorted by collection time (newest first).
• At least one filter required: Calls with no keyword / nicheName and no metric filter are rejected.
• Snapshot data: Records are aggregated from historical opportunity reports; new reports refresh the pool over time, but individual records are not real-time.
• Niche-level granularity: The output is niche / keyword level, not ASIN level. To dig into specific products inside a niche, hand off to linkfox-amazon-search, linkfox-keepa-product-search, etc.

User Expression & Scenario Quick Reference

Applicable — Niche-level reverse selection on the US Amazon market:

User Says	Scenario
"Low-competition niches", "newcomer-friendly", "brand-light"	Brand-density filter
"Brands are exiting", "old players retreating"	Negative brand-count YoY
"Fast-growing niche", "trending up", "≥100% YoY"	Search-volume YoY filter
"Mid-tier blue ocean", "low-price dominates but mid is scarce"	Price-tier share gap
"Premium-friendly", "high-income consumers"	Income tier + high-tier share
"Female / male / mixed market"	Gender dominance filter
"Parents / students / retirees / fitness enthusiasts"	Life-stage tag
"Strong size / quality / durability pain point"	Negative review topic + share
"Comfort-driven", "value-driven sellers"	Positive review topic + share
"Track all niches around brand X"	featureTopBrandsContains
"Q4 seasonal niches", "Prime Day window"	Peak month + peak volume

Not applicable — Use other tools instead:

• Need a comprehensive AI report on one keyword → linkfox-amazon-opportunity-report
• ASIN-level competitor research, sales estimation → SellerSprite / Keepa / Sorftime tools
• Real-time keyword ranking, search-term mining → ABA / SIF tools
• Marketplaces other than US → not yet supported by this tool
• Want to run group-by aggregation over niches via @智能数据查询 → unsupported (data is not warehoused)

Boundary judgment: When users describe selection criteria in business language and want matching candidate niches, this skill applies. When they hand you a specific keyword and want the full multi-dimensional analysis, use linkfox-amazon-opportunity-report. When they want to drill into ASINs / sellers within a niche, hand off to product-search tools.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

1. The functionality or purpose described in this skill does not match actual behavior
2. The skill's results do not match the user's intent
3. The user expresses dissatisfaction or praise about this skill
4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

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