An advanced AI location intelligence expert that automates site selection for physical businesses using real-time LBS data and strategic analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install smart-site-selection-skill
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install smart-site-selection-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Smart Site Selection AI Agent Skill?
The Smart Site Selection AI agent is a high-performance tool within the Openclaw Skills ecosystem designed to revolutionize how physical businesses choose their locations. By leveraging AMap Web LBS data and a sophisticated state machine, the skill moves beyond simple map searches to provide deep commercial insights. It synthesizes complex variables such as competitive density, traffic flow, and demographic targeting into a professional, interactive HTML report.
This skill acts as a bridge between high-level business strategy and ground-level geographic data. It ensures that every store opening decision is backed by scientific analysis, using a multi-dimensional evaluation matrix that includes macro-market trends and micro-level site assessments. By utilizing Openclaw Skills, developers and entrepreneurs can automate the traditionally manual process of site hunting and feasibility reporting.
Smart Site Selection AI Agent Skill Use Cases
- Identifying high-potential commercial zones for new retail storefronts or restaurants.
- Conducting competitive landscape analysis using dynamic radar charts to visualize market saturation.
- Generating data-driven pitch decks and feasibility reports for store expansion stakeholders.
- Refining business strategy by matching specific location characteristics to target customer demographics like students or corporate professionals.
How Smart Site Selection AI Agent Skill Works
- Performs an environment self-check to verify AMap API keys and silenty installs necessary Python visualization libraries.
- Executes a mandatory strategic validation phase to confirm the user's core customer positioning and business model.
- Retrieves high-precision geographic coordinates for the top three potential locations using specialized AMap POI plugins.
- Generates macro and micro commercial insights using AI-driven textual analysis based on current market trends.
- Triggers a backend Python engine to perform deep data scraping for transportation, business facilities, and competitor metrics.
- Compiles all data into a final interactive HTML report featuring ECharts visualizations and professional scoring.
Smart Site Selection AI Agent Skill Setup
To deploy this skill within the Openclaw Skills framework, ensure the following environment variable is set:
export AMAP_WEBSERVICE_KEY='your_amap_api_key_here'
The system will automatically manage dependencies, but you should ensure the following skills are installed in your agent environment:
- @AMap-Web/amap-lbs-skill
- searxng
- proactive-agent
Smart Site Selection AI Agent Skill Data Schema & Taxonomy
The skill organizes its output into a structured report format and manages internal data via a specific taxonomy:
| Data Component | Format | Source |
|---|---|---|
| Site Coordinates | Latitude/Longitude | AMap LBS |
| Strategic Insights | Markdown Text | AI Agent Reasoning |
| Competitive Metrics | Numerical Matrix | Python Calculation Engine |
| Visualization | ECharts / Static Maps | HTML/CSS Component |
Generated files follow the naming convention: [City]_[District]_[Category]_Professional_Business_Insight_Report.html and are stored in the current working directory.
Smart Site Selection AI Agent Skill Advanced Features
- Hard-stop strategic validation that prevents data fetching until business goals are clearly defined via the proactive-agent.
- Automated handoff between LLM-based reasoning and Python-based computational execution for high-accuracy scoring.
- Integrated competitive radar charts that visualize micro-business ecosystems in real-time.
- Robust error handling with fallback search granularity to ensure reports are generated even in data-sparse areas.
- Support for multi-agent workflows within the Openclaw Skills architecture for comprehensive market research.
SKILL.md
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