Find Nearby — Local Place Discovery

Find restaurants, cafes, bars, pharmacies, and other places near any location. Uses OpenStreetMap (free, no API keys). Works with:

• Coordinates from Telegram location pins (latitude/longitude in conversation)
• Addresses ("near 123 Main St, Springfield")
• Cities ("restaurants in downtown Austin")
• Zip codes ("pharmacies near 90210")
• Landmarks ("cafes near Times Square")

Quick Reference

# By coordinates (from Telegram location pin or user-provided)
python3 SKILL_DIR/scripts/find_nearby.py --lat <LAT> --lon <LON> --type restaurant --radius 1500

# By address, city, or landmark (auto-geocoded)
python3 SKILL_DIR/scripts/find_nearby.py --near "Times Square, New York" --type cafe

# Multiple place types
python3 SKILL_DIR/scripts/find_nearby.py --near "downtown austin" --type restaurant --type bar --limit 10

# JSON output
python3 SKILL_DIR/scripts/find_nearby.py --near "90210" --type pharmacy --json

Parameters

Common Place Types

restaurant, cafe, bar, pub, fast_food, pharmacy, hospital, bank, atm, fuel, parking, supermarket, convenience, hotel

Workflow

1. Get the location. Look for coordinates (latitude: ... / longitude: ...) from a Telegram pin, or ask the user for an address/city/zip.

2. Ask for preferences (only if not already stated): place type, how far they're willing to go, any specifics (cuisine, "open now", etc.).

3. Run the script with appropriate flags. Use --json if you need to process results programmatically.

4. Present results with names, distances, and Google Maps links. If the user asked about hours or "open now," check the hours field in results — if missing or unclear, verify with web_search.

5. For directions, use the directions_url from results, or construct: https://www.google.com/maps/dir/?api=1&origin=<LAT>,<LON>&destination=<LAT>,<LON>

Tips

• If results are sparse, widen the radius (1500 → 3000m)
• For "open now" requests: check the hours field in results, cross-reference with web_search for accuracy since OSM hours aren't always complete
• Zip codes alone can be ambiguous globally — prompt the user for country/state if results look wrong
• The script uses OpenStreetMap data which is community-maintained; coverage varies by region