Route planning
Skill cxcscmu/SkillLearnBench/skills/b1-one-shot-gemini-3.1-pro-preview/travel-planning/route-planning
[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.
npx -y skills add cxcscmu/SkillLearnBench --skill route-planningAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Algorithmic planning of itineraries avoiding specific transport modes and allocating time based on budget constraints.
SKILL.md
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Route Planning and Itinerary Construction
This skill focuses on sequentially constructing travel plans by selecting daily activities and accommodations, avoiding certain travel modes (e.g., no flights), and managing budget constraints.
Key Concepts
- Distance/Travel time estimation without flights (driving or ground transportation only).
- Day-by-day scheduling of activities (Meals, Attractions).
- Maintaining budget variables iteratively across multiple days.
Implementation Details
- Identify starting point and sequential destinations.
- Use a driving distance matrix to calculate time/costs.
- Iteratively assign activities and ensure costs are kept under constraints.
- Output final structured format (e.g., JSON).
import json
itinerary = {
'plan': [],
'data_sources': ['data/distance.csv', 'data/attractions.csv']
}
# Example daily assignment
day_plan = {
'day': 1,
'current_city': 'Minneapolis',
'transportation': 'Self-driving: from Minneapolis to Cleveland',
'breakfast': 'Local Cafe',
'lunch': 'Bistro',
'dinner': 'Fine Dining',
'attraction': 'City Park;Museum;',
'accommodation': 'Pet-Friendly Inn'
}
itinerary['plan'].append(day_plan)
with open('output/itinerary.json', 'w') as f:
json.dump(itinerary, f, indent=2)