Jiimore Niche Review from Keyword

This skill guides you on how to query and analyze Amazon niche market review data powered by Jiimore, helping Amazon sellers uncover consumer sentiment, pain points, and real demand signals from product reviews within niche markets.

Core Concepts

Niche Review Analysis aggregates and categorizes customer reviews across products in an Amazon niche market. Given a keyword, the system identifies the relevant niche markets, extracts review topics, classifies them as positive or negative, and shows how frequently each topic is mentioned. This enables sellers to understand what customers love, what frustrates them, and where product improvement opportunities exist.

Review types: Each review entry is classified as either "positive" or "negative", reflecting the overall sentiment of that review topic.

Mention percentage: The percentOfMentions value (0-1 scale, representing 0%-100%) indicates how frequently a particular topic appears across all reviews in the niche. A higher percentage means more customers are talking about that topic.

Supported Marketplaces

US (United States), JP (Japan), DE (Germany)

Default marketplace is US. Use US when the user does not specify a marketplace.

How to Invoke

• API Endpoint: POST /jiimore/getNicheReviewFromKeyword (complete params/response/error codes in references/api.md)
• Python Script: python scripts/amazon_niche_reviews_by_keyword.py '<JSON params>' [--inline]
• Cost constraint: This tool consumes credits; the same session and parameter combination is called only once by default, with a 24h local cache in the script. On failure or empty results, do not automatically retry with different keywords, pagination, or postal codes; inform the user about additional consumption before continuing to search.

Output strategy (script default behavior):

• Always write the full response to <cwd>/nexscope/<YYYY-MM-DD>/<session>/data/nexscope-jiimore-get-niche-review-from-keyword-<timestamp>.json (<cwd> is the working directory at script execution time, i.e. the current project directory in Claude Code; <session> is taken from the SESSION_ID env var, auto-grouped by user task; do not write to /tmp, error if current directory is not writable)
• Response body <= 8 KB: print full JSON to stdout after saving
• Response body > 8 KB: print only summary to stdout after saving (top-level fields, common counts like total/costToken, length of largest list field + first 3 samples)
• Add --inline to force full output to stdout (still saves to disk)

Data reading tip: Check the summary first to decide if it's enough; when specific fields are needed, prefer using jq or ConvertFrom-Json to extract from the saved json file on demand, avoiding loading the entire JSON into context.

Parameter Guide

Required Parameter

Parameter	Type	Description
keyword	string	The search keyword (max 1000 chars). Must be in the language of the target marketplace (English for US, German for DE, Japanese for JP)

Marketplace & Pagination

Parameter	Type	Default	Description
countryCode	string	US	Country code: US, JP, or DE
page	integer	1	Page number (starting from 1)
pageSize	integer	50	Results per page (10-100)

Sorting

Parameter	Type	Default	Description
sortField	string	unitsSoldT7	Field to sort by (see Sortable Fields below)
sortType	string	desc	Sort direction: desc (descending) or asc (ascending)

Sortable Fields:

Field	Description
unitsSoldT7	Units sold (7-day)
searchVolumeT7	Search volume (7-day)
searchVolumeGrowthT7	Search volume growth (7-day)
clickConversionRateT7	Click conversion rate (7-day)
searchConversionRateT7	Search conversion rate (7-day)
clickCountT7	Click count (7-day)
demand	Demand score
avgPrice	Average price
maximumPrice	Maximum price
minimumPrice	Minimum price
productCount	Product count
brandCount	Brand count
top5BrandsClickShare	Top 5 brands click share
top5ProductsClickShare	Top 5 products click share
clickCountT90	Click count (90-day)
clickConversionRateT90	Click conversion rate (90-day)
searchConversionRateT90	Search conversion rate (90-day)
searchVolumeT90	Search volume (90-day)
unitsSoldT90	Units sold (90-day)
unitsSoldGrowthT90	Units sold growth (90-day)
searchVolumeGrowthT90	Search volume growth (90-day)
returnRateT360	Return rate (360-day)
newProductsLaunchedT180	New products launched (180-day)
successfulLaunchesT180	Successful launches (180-day)
launchRateT180	Launch success rate (180-day)
acos	ACOS
profitRate50	Profit rate at 50% organic orders

Niche Filtering Parameters

All filter parameters follow a min/max range pattern. Values for percentage-based fields use a 0-1 scale (e.g., 0.05 = 5%).

Product & Brand Metrics:

Parameter	Type	Description
productCountMin / productCountMax	integer	Product count range
brandCountMin / brandCountMax	integer	Brand count range
avgPriceMin / avgPriceMax	number	Average price range

Sales & Search Volume:

Parameter	Type	Description
unitsSoldT7Min / unitsSoldT7Max	integer	Units sold (7-day) range
searchVolumeT7Min / searchVolumeT7Max	integer	Search volume (7-day) range
clickCountT7Min / clickCountT7Max	integer	Click count (7-day) range

Conversion & Click Rates (0-1 scale):

Parameter	Type	Description
clickConversionRateT7Min / clickConversionRateT7Max	number	Click conversion rate (7-day) range

Market Concentration (0-1 scale):

Parameter	Type	Description
top5BrandsClickShareMin / top5BrandsClickShareMax	number	Top 5 brands click share range
top5ProductsClickShareMin / top5ProductsClickShareMax	number	Top 5 products click share range
sponsoredProductsPercentageMin / sponsoredProductsPercentageMax	number	SP ad percentage range

Brand & Seller Age:

Parameter	Type	Description
avgBrandAgeMin / avgBrandAgeMax	number	Average brand age (current)
avgBrandAgeQoqMin / avgBrandAgeQoqMax	number	Average brand age (90-day)
avgBrandAgeYoyMin / avgBrandAgeYoyMax	number	Average brand age (360-day)
avgSellingPartnerAgeMin / avgSellingPartnerAgeMax	number	Average seller age (current)
avgSellingPartnerAgeQoqMin / avgSellingPartnerAgeQoqMax	number	Average seller age (90-day)
avgSellingPartnerAgeYoyMin / avgSellingPartnerAgeYoyMax	number	Average seller age (360-day)

New Product & Return Metrics (0-1 scale):

Parameter	Type	Description
launchRateT180Min / launchRateT180Max	number	Launch success rate (180-day) range
newProductRateT180	number	New product percentage (180-day) min
returnRateT360Min / returnRateT360Max	number	Return rate (360-day) range

Advertising:

Parameter	Type	Description
cpcMediumMin / cpcMediumMax	number	CPC (current) range

Usage Examples

1. Basic niche review lookup for a keyword

Analyze customer reviews in niche markets related to "yoga mat" on the US marketplace.

Parameters: {"keyword": "yoga mat", "countryCode": "US"}

2. Find niche reviews with high search volume

Show me niche market reviews for "wireless earbuds" where 7-day search volume is above 10000.

Parameters: {"keyword": "wireless earbuds", "countryCode": "US", "searchVolumeT7Min": 10000}

3. Low competition niches with review insights

Find review insights for "pet bed" niches where top 5 brands hold less than 30% click share.

Parameters: {"keyword": "pet bed", "countryCode": "US", "top5BrandsClickShareMax": 0.3}

4. Japanese market niche reviews

Analyze niche reviews for wireless earbuds on the Japan marketplace.

Parameters: {"keyword": "wireless earbuds", "countryCode": "JP"}

5. Sorted by demand score

Show niche reviews for "kitchen organizer" sorted by demand score in descending order.

Parameters: {"keyword": "kitchen organizer", "sortField": "demand", "sortType": "desc"}

6. Filter by new product success rate

Find niches for "phone case" where the 180-day new product launch success rate is above 20%.

Parameters: {"keyword": "phone case", "launchRateT180Min": 0.2}

7. Low return rate niches

Show review topics for "water bottle" niches with return rates below 5%.

Parameters: {"keyword": "water bottle", "returnRateT360Max": 0.05}

Display Rules

1. Present data clearly: Show review topics in a well-organized table. Include the niche name, review type (positive/negative), topic, mention percentage, and a review example
2. Percentage formatting: Convert 0-1 scale values to percentages for display (e.g., 0.15 -> 15%)
3. Sentiment separation: When presenting results, group or clearly label positive vs. negative reviews so users can quickly identify opportunities and pain points
4. Actionable insight framing: While showing data objectively, highlight high-mention-percentage negative reviews as potential product improvement opportunities, and high-mention-percentage positive reviews as features to emphasize in listings
5. Volume notice: When results are large, show the most relevant data first and remind users about pagination options
6. Error handling: When a query fails, explain the reason and suggest adjusting the keyword or filter criteria
7. Language reminder: If a user provides a keyword in the wrong language for the target marketplace, remind them to use the marketplace's native language (English for US, German for DE, Japanese for JP)

User Expression & Scenario Quick Reference

Applicable -- Consumer review and sentiment analysis within Amazon niche markets:

User Says	Scenario
"What do customers say about XX"	Niche review topic lookup
"Customer pain points for XX"	Negative review analysis
"What features do buyers love in XX"	Positive review analysis
"Review sentiment for XX niche"	Full sentiment breakdown
"Consumer demand insights for XX"	Demand signal extraction from reviews
"Common complaints about XX products"	Negative topic mining
"What makes XX products popular"	Positive topic mining
"Niche market review analysis"	General niche review exploration

Not applicable -- Needs beyond niche review analysis:

• Individual ASIN review analysis (this tool works at the niche/market level)
• Keyword search volume trends without review context (use ABA data tools instead)
• Product listing optimization or copywriting
• Advertising strategy and PPC management
• Sales estimation or revenue forecasting

Boundary judgment: When users say "market research" or "product opportunity", if their intent focuses on understanding consumer sentiment, review topics, and pain points within a niche market, this skill applies. If they are asking about search volume trends, pricing strategy, or sales data without review context, it does not apply.

Authentication

Set the NEXSCOPE_API_KEY environment variable. If credentials are missing or expire, visit https://www.nexscope.ai/help/skills-external-access?co-from=skillNS to top up credits.