Extract meals, hotels, costs, and notices from itinerary data into structured JSON.
npx skills add https://github.com/JeffTermited/packgo-travel --skill details
Please help me install this Agent Skill. Skill: details Source: https://github.com/JeffTermited/packgo-travel/tree/main/server/agents/_subskills/details Command: npx skills add https://github.com/JeffTermited/packgo-travel --skill details
The Details Skill automates extraction of travel itinerary details from raw data, transforming scattered notes into structured, JSON-friendly outputs that include meals, hotels, costs, and notices.
Provide the itinerary data and request a combined details extraction to receive meals, hotels, costs, and notices in a structured JSON payload.
High-intent search queries and answers about installing and using this skill.
To extract trip details, apply the extraction process to daily agendas or JSON trip objects to produce structured outputs for meals, hotels, costs, and notices. This ensures strict data fidelity and JSON schema compliance without fabricating missing prices or hotel details.
Yes, you can parse daily agendas to identify meal names, types, cuisines, and restaurants, alongside hotel stars, facilities, and locations. The extraction transforms scattered itinerary notes into structured, JSON-friendly outputs ready for client reporting.
To generate structured travel costs and notices, process the itinerary data to identify included, excluded, and additional costs with notes, plus preparation, cultural, and health guidance. It strictly avoids fabricating prices or hotel details not present in the source.
Yes, this itinerary extraction approach works directly with JSON trip objects and other itinerary formats. It processes the provided data to output a combined structured payload containing meal, hotel, cost, and notice details compliant with JSON schema rules.
The best way to structure travel itinerary data for a recommendation engine is to extract meals, hotels, costs, and notices into a structured JSON payload. This ensures strict data fidelity and schema compliance, providing a ready-to-use output without fabricated details.
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