A comprehensive Amazon product research tool built on the SellerSprite MCP service and Claude Skills, providing full-listing penetration analysis, category analysis, keyword research, negative review analysis, and market research.
This skill enables AI agents (such as Claude Code, Cursor, or Codex) to perform deep competitive analysis on Amazon products using the SellerSprite MCP service. It covers eight core capabilities: detailed listing analysis (revenue, pricing, BSR, review volume, review rating, listing age, and seller type), category analysis (parent and fine-grained subcategories), market analysis (average price, average revenue, top brand share, average rating, and average review count), keyword analysis (search volume, trend, and competitors), competition analysis (competitor count and distribution), negative review analysis (review percentage, main complaints, and rating distribution), keyword expansion, and product tracking. Designed for Amazon sellers and market researchers, it saves hours of manual data gathering and enables data-driven decision-making directly from the agent's workflow.
npx skills add Ethan-paseya/amazon-sellersprite-research-MCP-skillInstalled? Explore more Research & Data Analysis skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
An Amazon product research skill providing full-listing penetration analysis reports, comprehensive category analysis, keyword analysis, negative review analysis, market research, and more. OpenClaw / Claude Code agent skill, Amazon SellerSprite research MCP agent skill. A comprehensive Amazon competitive analysis toolset based on the SellerSprite MCP service and Claude Skills.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.