Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Plans multi-stop journeys by calculating route feasibility, total travel time, and segment-by-segment distances. Accepts waypoints with latitude/longitude coordinates and optional constraints like transport mode and time budget. Returns a structured analysis including whether the itinerary fits within the given time.
Combines feasibility checking and time budgeting in a single call, avoiding the need to manually chain multiple routing queries.
When to use it
- Checking if a Paris day trip fits in 6 hours on foot
- Planning a business meeting route with a return leg
- Validating a cycling tour across multiple neighborhoods
- Estimating total walking time for a city sightseeing itinerary
- Optimizing stop order for a multi-client sales visit
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Can I visit the Eiffel Tower, Louvre, Notre-Dame, and Sacré-Cœur on foot in 6 hours?
- 1Extracts coordinates for each landmark as waypoints
- 2Sets transport mode to 'foot' and time_budget to '6 hours'
- 3Calls the Camino journey API with the waypoint array and constraints
- 4Parses the response for feasibility flag, total_time_formatted, and route_segments
- 5Reports whether the itinerary is achievable and lists per-segment durations
Journey is feasible: 4 stops, ~5.2 km, estimated 74 minutes walking time, well within the 6-hour budget
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.