SuperAPI AI Directory
Partner with us中文AI newsFavorites
Skillslinkfox-amazon-opportunity-search-by-metrics

linkfox-amazon-opportunity-search-by-metrics

Marketing & growth

View repository

Updated 10-15 13:39

About this skill

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.

How to call

  • API endpoint：POST /amazon/opportunity/searchByMetrics(for full parameters/responses/error codes, see references/api.md）
  • Python script：python scripts/amazon_opportunity_screener.py '<JSON 参数>' [--inline]
  • Cost constraints: This tool consumes compute; by default, the same parameter combination in the same session is called only once, and the script has a 24h local cache. On failure/empty results, do not automatically switch keywords, paginate, or change zip codes to probe repeatedly; if further retrieval is needed, first explain to the user that it will incur additional consumption.

Output strategy (default script behavior)：

  • Alwayswrite the full response to <cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-amazon-opportunity-search-by-metrics-<timestamp>.json（<cwd> is the working directory when the script runs, which in Claude Code is the current project directory;<session> is taken from the environment variable SESSION_ID, automatically aggregated according to the user's task;writing to /tmp is prohibited, and if the current directory is not writable, report an error)
  • Response body ≤ 8 KB: after writing to disk, print the full JSON to stdout
  • Response body > 8 KB: after writing to disk, stdout outputs only a summary (top-level fields, common counts such as total/costToken, length of the largest list field + first 3 samples)
  • Add --inline to force full printing to stdout (also written to disk)

Data reading suggestions: first look at the summary to judge whether it is sufficient; when specific fields are needed, prefer to use jqorConvertFrom-Json to extract on demand from the saved json file, avoiding bringing the entire JSON into context.

Resolve authentication and compute issues

When the following abnormal situations occur, use references/onboarding.md to guide resolution:

Abnormal situations

  • API Key not configured: the environment variable is not configured LINKFOX_AGENT_API_KEY, and LINKFOXAGENT_API_KEY。
  • Response 401 or 402 status code
  • Response indicates insufficient compute or balance: the message contains "insufficient compute balance/insufficient billing/insufficient balance/quota exceeded/insufficient balance/package expired/recharge required/please recharge", or content with a similar meaning.

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

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

2. Newcomer-friendly low-competition niches

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

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

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

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

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

5. Pain-point entry — strong size complaints

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

6. Premium-friendly female-driven niches

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

7. Q4 seasonal niches with ≥100k peak search

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

8. Track niches around a known competitor brand

{"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 ("Current filters: brand count ≤ 20 and search volume YoY ≥ 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.

Compute consumption rules

Billed by dynamic rules: compute consumed = 1.5 × (N ≤ 25 ? 5 : 5 + ceil((N - 25) / 5)). N is the number of data records actually returned by the API; even if N = 0, as long as the API succeeds, it is billed as 15000.

Important: the service of this skill is calculated dynamically by a multiplier and may consume a large amount of compute at once. You must remind the user and let the user decide whether to continue.

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.

For more high-quality, professional cross-border e-commerce skills, visit LinkFox Skills.