An AI-powered commercial site assessment tool that harnesses real-time map API data to generate multi-dimensional store location reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install store-location-analysis
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 store-location-analysis 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 Store Location Analysis Assistant?
The Store Location Analysis skill is a professional-grade commercial site assessment tool powered by Gaode (AMAP) Map Web Service APIs. By feeding a target address and business type into the system, developers can instantly fetch geographical coordinates, evaluate surrounding Point of Interest (POI) density, assess real-time traffic flow, and identify local competitors. Integrating Openclaw Skills into your automated business workflow provides a robust spatial analytics engine that translates complex location metadata into straightforward commercial grades.
This workflow-oriented agent streamlines the entire site vetting process. It replaces manual street-level surveying with a automated data-gathering sequence, rating candidate locations across six core indexes: foot traffic proxy indicators, demographic density, customer flow patterns, spending power, competitive threat, and business environment. The resulting output is an beautifully rendered, interactive HTML dashboard suitable for business presentations and fast decision-making.
Store Location Analysis Assistant Use Cases
- Franchise & Retail Expansion: Instantly evaluate prospective shopfronts for cafes, bubble tea stores, or restaurants to select the highest-traffic areas.
- Commercial Competitive Audits: Assess competitor saturation by mapping out identical store types within custom radiuses.
- Geospatial Real Estate Analysis: Assist agents and investors in generating data-backed viability reports for commercial spaces.
- Pre-investment Site Screening: Speed up due diligence by filtering out poorly connected or overly competitive neighborhoods before signing leases.
How Store Location Analysis Assistant Works
- Input Gathering: The AI agent prompts the user to provide a Chinese target address (or landmark), store type (e.g., convenience store), and an optional search radius (defaults to 1000 meters).
- Environment & Key Check: Verifies that the
AMAP_KEYenvironment variable is exported or supplied via script arguments. - Geospatial Querying: The
analyze_location.pyscript geocodes the physical address, queries nearby POIs, and calls traffic status APIs to generate real-time road congestion scores. - Dimension Evaluation: The engine applies preset or custom weights to rate the location out of 100 based on six spatial characteristics.
- Report Rendering: The
generate_report.pyscript parses the analytical output JSON and outputs an interactive HTML report containing responsive Chart.js visual graphics. - Result Presentation: The skill uses the present_files capability to return the ready-to-view HTML file directly to the user's workspace.
Store Location Analysis Assistant Setup
First, configure your environment with your Gaode Maps Web Service API Key. Follow these CLI commands to execute site queries and compile reports:
# Export your Gaode Maps Web Service Key
export AMAP_KEY="your_gaode_maps_api_key_here"
# Run the location analysis script
python "scripts/analyze_location.py" \
--address "Sanlitun Taikoo Li, Beijing" \
--store-type "Coffee Shop" \
--radius 1000 \
--output "./output/analysis_result.json"
# Generate the interactive HTML report
python "scripts/generate_report.py" \
--input "./output/analysis_result.json" \
--output "./output/location_analysis_report.html"
Store Location Analysis Assistant Data Schema & Taxonomy
The Store Location Analysis tool structures its scoring evaluations according to the following analytical schema:
| Assessment Category | Weight | Data Extraction & Metric Logic |
|---|---|---|
| Foot Traffic | 30% | Based on real-time road congestion indexes and local commercial grid density. |
| Target Demographics | 20% | Weighted sum of surrounding residential buildings, offices, and academic institutions. |
| Transit Accessibility | 15% | Density of surrounding public transit hubs, subway entrance points, and bus stops. |
| Local Spending Power | 15% | Density of upscale shopping centers, entertainment facilities, and fine dining. |
| Competitor Distribution | 15% | Counts identical business categories (0 is average, 1-3 is optimal, >15 penalizes scores). |
| Business Environment | 5% | Supplemental public services, healthcare centers, and neighborhood stability. |
Data Taxonomy & Outputs
analysis_result.json: An structured JSON database consisting ofgeo,surroundings(POI grids),traffic(road speed metrics),competitors(distance matrix), andscoringobjects.选址分析报告.html: A self-contained web dashboard with interactive charts, grading gauges, and categorized P0/P1/P2 actionable checklists.
Store Location Analysis Assistant Advanced Features
- Adjustable Score Weights: Modify the
weightsdictionary inside the corecompute_scoresPython function inanalyze_location.pyto match niche business types. - Adaptive Search Radiuses: Scale analysis radiuses automatically based on retail category guidelines (e.g., 300m for community convenience stores, up to 5000m for regional supermarkets).
- Resilient Retry Mechanism: Integrated auto-retries for network calls (3 attempts with 1-second delay) to counter API gateway timeouts.
- Responsive Analytical Dashboard: Rendered reports feature Chart.js components, rating gauges, competitive mapping tables, and priority lists.
SKILL.md
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