travel-map

Travel Map

⚠️ Connection Required: ALWAYS CHECK FIRST

Before doing ANYTHING else in this skill, call ping on the Immich MCP server.

• If ping succeeds → proceed with the skill normally.
• If ping fails or the MCP tools are not available → STOP. Do not continue. Tell the user:

❌ Immich is not connected. This plugin needs a running Immich MCP server to work.

Run /setup-immich-photo-manager to configure your Immich connection. You'll need:

1. Your Immich server URL (e.g., http://192.168.1.100:2283)
2. An Immich API key (how to create one)
3. The MCP server configured (see /setup-immich-photo-manager)

Nothing in this plugin will work until the connection is configured.

Do NOT skip this check. Do NOT try to run any other tool first. Always ping, always block if it fails.

Generate an interactive HTML map showing all locations where photos were taken. Clusters photos by geographic proximity, shows photo counts and date ranges per location, and optionally links to Immich albums.

When to Use

• Visualize all travel destinations at a glance
• Discover forgotten trips (photos with GPS you didn't remember)
• Plan which geographic albums to create
• Share a "places I've been" page

Map Generation Workflow

Step 1: Extract GPS Data

Get all geotagged photos:

SELECT
  "id",
  ("exifInfo"->>'latitude')::float as lat,
  ("exifInfo"->>'longitude')::float as lng,
  "localDateTime",
  "originalPath",
  ("exifInfo"->>'city') as city,
  ("exifInfo"->>'state') as state,
  ("exifInfo"->>'country') as country
FROM asset
WHERE "deletedAt" IS NULL
  AND "exifInfo"->>'latitude' IS NOT NULL
  AND ("exifInfo"->>'latitude')::float != 0
ORDER BY "localDateTime";

Or use the MCP tool get_map_markers for a lighter dataset.

Step 2: Cluster by Location

Group nearby photos into location clusters:

from collections import defaultdict
import math

def haversine(lat1, lng1, lat2, lng2):
    """Distance in km between two GPS points."""
    R = 6371
    dlat = math.radians(lat2 - lat1)
    dlng = math.radians(lng2 - lng1)
    a = math.sin(dlat/2)**2 + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlng/2)**2
    return R * 2 * math.asin(math.sqrt(a))

def cluster_locations(photos, radius_km=15):
    """Simple greedy clustering by distance."""
    clusters = []
    for photo in photos:
        placed = False
        for cluster in clusters:
            if haversine(photo.lat, photo.lng, cluster.center_lat, cluster.center_lng) < radius_km:
                cluster.add(photo)
                placed = True
                break
        if not placed:
            clusters.append(Cluster(photo))
    return clusters