route-planning
Algorithmic planning of itineraries avoiding specific transport modes and allocating time based on budget constraints.
- License 许可
- MIT
- 最近更新
- 2026-10-02
- Stars
- ★ 93
- Forks
- 8
01 前言 Front Matter
- name
- route-planning
- description
- Algorithmic planning of itineraries avoiding specific transport modes and allocating time based on budget constraints.
03 SKILL.md 正文
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)
04 同仓库更多 Skills
object_counter
Count occurrences of an object in the image using computer vision algorithm.
travel-planning-workflow
Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.
bundled-dataset-search-tool
invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.
glm-output
Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final /root/metrics.json.
glm-calibration
Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.
glm-basics
A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.