real-estate

What This Skill Does

The real-estate skill is a specialized agent capability within the Camino AI ecosystem designed to provide comprehensive location intelligence for prospective home buyers, renters, and real estate professionals. By leveraging high-fidelity geospatial data, it evaluates any given address by surfacing essential local amenities including schools, public transit stations, grocery stores, parks, and restaurants. Beyond simple point-of-interest lookups, the skill integrates walkability insights to provide a holistic view of the neighborhood's livability. Whether you are performing a quick check on a potential apartment or conducting a deep dive into an investment property's surroundings, this skill automates the data aggregation process, saving hours of manual research.

Installation

To install this skill, you can use the OpenClaw standard hub or the provided npm packages. The recommended method is via clawhub for seamless environment integration:

npx clawhub@latest install real-estate

Alternatively, if you prefer using the direct repository, you can add it via your terminal:

npx skills add https://github.com/barneyjm/camino-skills --skill real-estate

Ensure you have configured your CAMINO_API_KEY in your environment variables at ~/.claude/settings.json to allow the skill to interface with the location API. You can initiate a trial for testing by following the steps outlined in the developer documentation, granting you 25 free queries.

Use Cases

• Renters: Quickly evaluate if a potential apartment has grocery stores within walking distance or is near major transit lines to simplify your commute.
• Home Buyers: Assess the proximity and quality of nearby schools and public parks to ensure the neighborhood aligns with long-term family needs.
• Real Estate Investors: Generate standardized neighborhood context reports for multiple potential investment properties to compare walkability and amenity density.
• Relocation Assistance: Use the agent to automatically scan candidate cities and identify neighborhoods that satisfy specific lifestyle criteria like high restaurant density or proximity to green space.

Example Prompts

1. "Evaluate the neighborhood at 123 Maple Street, Chicago. I need to know about nearby parks, public transit accessibility, and how walkable the area is for a daily commute."
2. "I am looking at a property in San Francisco. Please check for grocery stores and high-rated restaurants within a 500-meter radius of the coordinates 37.7749, -122.4194."
3. "Is the house located at 456 Oak Avenue near good schools? Give me a full report on the surrounding amenities including transit and grocery options."

Tips & Limitations

• Radius Tuning: For dense urban environments like New York City, use a smaller radius (e.g., 500 meters) to avoid getting an overwhelming number of results. For suburban areas, a 1500-2000 meter radius is often more appropriate.
• API Limits: Be mindful of your usage tiers; the free tier offers 1,000 calls per month. For high-volume automated analysis, ensure you are using a paid API key.
• Data Freshness: While the Camino AI data is highly accurate, always cross-reference critical data points like school districts or school ratings with local government sources before making a financial commitment.
• Geocoding: If the address is ambiguous, the skill performs a lookup. If the result is not what you expected, provide coordinates (lat/lon) directly for 100% precision.