linkfox-amazon-opportunity-report

Amazon Market Opportunity Report

This skill guides you on how to generate comprehensive AI-powered market insight reports for Amazon keywords, helping sellers make data-driven product selection and market entry decisions.

Core Concepts

This tool generates a comprehensive business insight report by analyzing six core dimensions of an Amazon keyword:

1. Market Potential - Search volume, demand trends, and growth opportunity
2. Product Characteristics - Common product attributes, materials, features
3. User Reviews - Customer sentiment, pain points, and satisfaction drivers
4. Customer Profile - Buyer demographics, preferences, and behavior patterns
5. Search Trends - Keyword popularity trajectory and seasonality
6. Pricing Analysis - Price distribution, competitive pricing landscape

The report is generated by AI from real-time Amazon data and delivered as a structured Markdown document. It is a snapshot analysis for decision support, not a real-time monitoring tool.

Parameters

Parameter	Type	Required	Description	Default
site	string	Yes	Amazon marketplace code (currently US only)	US
keyword	string	Yes	The search keyword to analyze	-

Supported Marketplaces

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

API Usage

This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, and response structure. You can also execute scripts/amazon_opportunity_report.py directly to run queries.

Usage Examples

1. Basic Market Opportunity Report

帮我搜索美国站，关键词是 hair mousse travel size 的商业洞察报告

2. Niche Product Research

帮我生成美国站关键词 magnetic shelves for whiteboard 的商业洞察报告

3. Trend-Based Product Selection

我想了解美国站 solar power ac unit 这个关键词的商业洞察，帮我生成报告

Display Rules

1. Present the report directly: The API returns a Markdown-formatted report in the stdout field - display it as-is to the user
2. No subjective advice: Present the AI-generated analysis without adding your own business recommendations
3. Clarify scope: Remind users the report is based on a point-in-time snapshot and serves as a reference for decision-making
4. Error handling: When a query fails, explain the reason based on the msg field and suggest checking the keyword or trying again
5. No secondary analysis: This tool's output cannot be fed into data query tools for further processing - inform users if they attempt this

Important Limitations

• US only: Currently only supports the United States marketplace (site = US)
• Non-structured output: Returns a Markdown report, not structured JSON data. Cannot be used with data query tools for secondary aggregation or analysis
• Processing time: Report generation involves AI analysis and may take longer than simple data lookups
• Snapshot data: Reports reflect data at the time of generation and are not continuously updated

User Expression & Scenario Quick Reference

Applicable - Comprehensive keyword-level market analysis:

User Says	Scenario
"Help me analyze this keyword's market opportunity"	Market opportunity assessment
"Generate a market insight report for XX"	Full insight report
"What's the competitive landscape for XX"	Competition & pricing analysis
"Consumer profile for XX product"	Customer behavior insights
"Is XX keyword worth entering"	Market entry evaluation
"选品报告", "商业洞察"	Chinese market research requests

Not applicable - Needs beyond comprehensive report generation:

• Real-time keyword ranking tracking (use ABA or SIF tools)
• Individual product detail lookup (use product detail tools)
• Historical price tracking (use Keepa)
• Review-level analysis for specific ASINs (use review tools)
• Advertising / PPC strategy

Boundary judgment: When users say "market analysis" or "product research", if they want a holistic, multi-dimensional overview of a keyword's market opportunity, this skill applies. If they need specific data points (exact sales numbers, individual product details, keyword rankings over time), direct them to the appropriate specialized tool.

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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Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/amazon_opportunity_report.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

• High field count per record, or fields you don't need
• Batch/paginated results (multiple items per call)
• Long-text fields (descriptions, reviews, HTML, time series)
• Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read.

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