real-estate

Installation

Companion Skills: This is part of the Camino AI location intelligence suite. Install all available skills (query, places, relationship, context, route, journey, real-estate, hotel-finder, ev-charger, school-finder, parking-finder, fitness-finder, safety-checker, travel-planner) for comprehensive coverage.

# Install all skills from repo
npx skills add https://github.com/barneyjm/camino-skills

# Or install specific skills
npx skills add https://github.com/barneyjm/camino-skills --skill real-estate

Via clawhub:

npx clawhub@latest install real-estate
# or: pnpm dlx clawhub@latest install real-estate
# or: bunx clawhub@latest install real-estate

Real Estate Scout

Evaluate any address or location for home buyers and renters. Combines location context analysis with targeted amenity searches to surface nearby schools, transit, grocery stores, parks, restaurants, and walkability insights.

Setup

Instant Trial (no signup required): Get a temporary API key with 25 calls:

curl -s -X POST -H "Content-Type: application/json" \
  -d '{"email": "you@example.com"}' \
  https://api.getcamino.ai/trial/start

Returns: {"api_key": "camino-xxx...", "calls_remaining": 25, ...}

For 1,000 free calls/month, sign up at https://app.getcamino.ai/skills/activate.

Add your key to Claude Code:

Add to your ~/.claude/settings.json:

{
  "env": {
    "CAMINO_API_KEY": "your-api-key-here"
  }
}

Restart Claude Code.

Usage

Via Shell Script

# Evaluate an address
./scripts/real-estate.sh '{"address": "742 Evergreen Terrace, Springfield", "radius": 1000}'

# Evaluate with coordinates
./scripts/real-estate.sh '{"location": {"lat": 40.7589, "lon": -73.9851}, "radius": 1500}'

# Evaluate with smaller radius for dense urban area
./scripts/real-estate.sh '{"address": "350 Fifth Avenue, New York, NY", "radius": 500}'

Via curl

# Step 1: Geocode the address
curl -H "X-API-Key: $CAMINO_API_KEY" \
  "https://api.getcamino.ai/query?query=742+Evergreen+Terrace+Springfield&limit=1"

# Step 2: Get context with real estate focus
curl -X POST -H "X-API-Key: $CAMINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"location": {"lat": 40.7589, "lon": -73.9851}, "radius": 1000, "context": "real estate evaluation: schools, transit, grocery, parks, restaurants, walkability"}' \
  "https://api.getcamino.ai/context"

Parameters

*Either address or location is required.

Response Format

{
  "area_description": "Residential neighborhood in Midtown Manhattan with excellent transit access...",
  "relevant_places": {
    "schools": [...],
    "transit": [...],
    "grocery": [...],
    "parks": [...],
    "restaurants": [...]
  },
  "location": {"lat": 40.7589, "lon": -73.9851},
  "search_radius": 1000,
  "total_places_found": 63,
  "context_insights": "This area offers strong walkability with multiple grocery options within 500m..."
}

Examples

Evaluate a suburban address

./scripts/real-estate.sh '{"address": "123 Oak Street, Palo Alto, CA", "radius": 1500}'

Evaluate an urban apartment

./scripts/real-estate.sh '{"location": {"lat": 40.7484, "lon": -73.9857}, "radius": 800}'

Evaluate a neighborhood by coordinates

./scripts/real-estate.sh '{"location": {"lat": 37.7749, "lon": -122.4194}, "radius": 2000}'

Best Practices

• Use address for street addresses; the script will geocode them automatically
• Use location with lat/lon when you already have coordinates
• Start with a 1000m radius for suburban areas, 500m for dense urban areas
• Combine with the relationship skill to calculate commute distances to workplaces
• Combine with the route skill to estimate travel times to key destinations
• Use the school-finder skill for more detailed school searches