itinerary-planning
How to construct a 7-day, pet-friendly travel itinerary for three Ohio cities starting from Minneapolis without using flights. Use this skill for planning, organizing the itinerary data, and formatting the final JSON output.
- License 许可
- MIT
- 最近更新
- 2026-10-02
- Stars
- ★ 93
- Forks
- 8
01 前言 Front Matter
- name
- itinerary-planning
- description
- How to construct a 7-day, pet-friendly travel itinerary for three Ohio cities starting from Minneapolis without using flights. Use this skill for planning, organizing the itinerary data, and formatting the final JSON output.
03 SKILL.md 正文
Itinerary Planning Skill
Overview
This skill outlines the process for building a 7-day, non-flight travel itinerary across three cities in Ohio, starting from Minneapolis.
Planning Steps
- Identify Cities: Select three cities in Ohio from
data/background/citySet.txtorcitySet_with_states.txt. - Timeline: Plan the 7 days (March 17th - March 23rd, 2022).
- Route: Plan the driving route from Minneapolis to the first Ohio city and between subsequent Ohio cities.
- Logistics: Ensure accommodations are marked as pet-friendly (check
data/accommodations/). - Cuisine: Plan daily meals (American, Mediterranean, Chinese, Italian).
- Activities: Select attractions from
data/attractions/.
JSON Format
Your final output must be a single JSON object at /app/output/itinerary.json with:
plan: Array of 7 day objects (day, current_city, transportation, breakfast, lunch, dinner, attraction, accommodation).data_sources: Array of file paths used for the plan.
Constraints
- NO FLIGHTS: Use road travel only.
- BUDGET: Maximum $5,100.
- PETS: Accommodations MUST be pet-friendly.
- DURATION: 7 days.
- LOCATIONS: Three cities in Ohio.
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.