Event collection
Skill Makiato1999/agentic-travel/.claude/skills/event-collection
An LLM-powered multi-agent business travel planning system that integrates RAG knowledge retrieval, user preference memory, real-time information querying, and itinerary generation.
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Use this skill when the user provides travel details like origin, destination, dates, purpose, or when planning a trip. It extracts structured event information for itinerary planning.
SKILL.md
3.0 KB, 980 tokens by cl100k_base, as published. Nobody here has run it
Event Collection Skill
用于从用户输入中提取本次行程的结构化信息,供 itinerary_planning 等后续 agent 使用。
通常由 IntentionAgent 自动调度,配合 plan-trip 技能使用。
核心原则
- 只提取当前这一次行程的信息,不要把用户历史偏好、家庭住址、常住地当作本次行程事实
- 如果用户输入里明确出现了出发地、目的地、日期、时长,必须以当前输入中的显式信息为准,优先级高于背景信息
- 只有当用户当前输入没有明确说出发地时,才允许根据背景信息中的
home_location推断origin - 用户说“常住苏州”“我家在杭州”这类信息,默认属于背景信息/偏好,不等于本次出发地,除非用户明确说“从苏州出发”
- 如果用户当前输入中明确说“从南京去成都”,则
origin必须是南京,不能被家庭住址或历史偏好覆盖
提取字段
请尽可能提取以下结构化字段:
origin- 出发地destination- 目的地start_date- 出发日期(YYYY-MM-DD)end_date- 返程日期(YYYY-MM-DD)duration_days- 行程天数return_location- 返程地trip_purpose- 行程目的missing_info- 缺失字段列表extracted_count- 成功提取字段数量summary- 一句简要摘要
日期处理规则
- 当前时间由调用方提供
- 用户说“2月27日”“2.27”等相对日期时,需要根据当前时间推断完整年月日
- 用户说“明天”“后天”“下周”等相对时间时,需要换算成具体日期
- 所有日期必须输出为完整的
YYYY-MM-DD - 如果用户给了
duration_days = N且给了start_date,但没有明确end_date,则按包含起始日计算:- 1天 ->
end_date = start_date - 2天 ->
end_date = start_date + 1天 - 4天 ->
end_date = start_date + 3天 - 一般规则:
end_date = start_date + (duration_days - 1)
- 1天 ->
特殊处理
- 对于“北京一日游”这类:
origin和destination都设为北京 - 对于“一日游”:
duration_days = 1 - 如果用户没明确说
return_location,默认return_location = origin - “常住地 / 家在某地 / 喜欢某类酒店 / 饮食习惯”不属于
trip_purpose,不要混入origin/destination/trip_purpose
输出格式
严格输出 JSON:
{
"origin": "北京",
"destination": "上海",
"start_date": "2026-04-13",
"end_date": "2026-04-16",
"duration_days": 4,
"return_location": "北京",
"trip_purpose": "商务出行",
"missing_info": [],
"extracted_count": 7,
"summary": "2026年4月13日至16日从北京到上海的4天商务出行"
}
缺失的信息在 missing_info 中列出,对应字段设为 null。