Store Benchmark analysis tool. Compare with other stores in the group/region and analyze store level, ranking changes, unit price Γ joint rate matrix quadrant. Core capabilities: 1. Multi-dimensional comparison scope (all groups, regions, provinces, cities) 2. Store level evaluation (based on sales, number of orders, unit price per customer, and linkage rate) 3. Tracking of ranking changes (ranking in this period vs. ranking in the previous period) 4. Unit price Γ linkage rate matrix quadrant analysis (identification door...
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install retail-store-benchmark-analysisOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install retail-store-benchmark-analysisβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/retail-store-benchmark-analysis/π‘Extract and place the folder at the path above, then restart your agent.
Category
Data & AnalyticsWhat Store Benchmark Analysis can do for your AI workflow
Store benchmark analysis tool directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Store Benchmark Analysis
Help me get started with Store Benchmark Analysis
Explains what Store Benchmark Analysis does, walks through the setup, and runs a quick demo based on your current project
Use Store Benchmark Analysis to store Benchmark analysis tool
Invokes Store Benchmark Analysis with the right parameters and returns the result directly in the conversation
What can I do with Store Benchmark Analysis in my data & analytics workflow?
Lists the top use cases for Store Benchmark Analysis, with example commands for each scenario
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Store Benchmark Analysis extends your AI assistant with the ability to store Benchmark analysis tool. Compare with other stores in the group/region and analyze store level, ranking changes, unit price Γ joint rate matrix quadrant. Core capabilities: 1. Multi-dimensional comparison scope (all groups, regions, provinces, cities) 2. Store level evaluation (based on sales, number of orders, unit price per customer, and linkage rate) 3. Tracking of ranking changes (ranking in this period vs. ranking in the previous period) 4. Unit price Γ linkage rate matrix quadrant analysis (identification door... Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Store Benchmark Analysis as its underlying capability.
Store Benchmark Analysis works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Store Benchmark Analysis once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
Getting started with Store Benchmark Analysis takes about two minutes. Place the skill at `~/.claude/skills/retail-store-benchmark-analysis/` (personal, all projects) or `.claude/skills/retail-store-benchmark-analysis/` (project-specific), then restart your AI client. From that point, typing `/retail-store-benchmark-analysis` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.
Store Benchmark Analysis has 62 installs and is part of the growing Data & Analytics skill ecosystem on DiscoverAISkills. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/retail-store-benchmark-analysis/ for personal use (all projects), or .claude/skills/retail-store-benchmark-analysis/ for project-specific use. Restart your AI client, then invoke with /retail-store-benchmark-analysis or let the AI discover it automatically.
Store Benchmark Analysis supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Store Benchmark Analysis is free to install. Check the repository for licensing information.
Store Benchmark analysis tool. Compare with other stores in the group/region and analyze store level, ranking changes, unit price Γ joint rate matrix quadrant. Core capabilities: 1. Multi-dimensional comparison scope (all groups, regions, provinces, cities) 2. Store level evaluation (based on sales, number of orders, unit price per customer, and linkage rate) 3. Tracking of ranking changes (ranking in this period vs. ranking in the previous period) 4. Unit price Γ linkage rate matrix quadrant analysis (identification door...
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