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meetupmeetup 搜索

Agent Skill

meetup 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

5,067

周安装

207

GitHub Stars

公开资料未说明

下载量

1,639
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:meetup(meetup 搜索)
来源仓库:https://github.com/clawnewsde/meetup
安装命令:
openclaw skills install meetup
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install meetup

简介

查找附近 OpenClaw 聚会与 AI 社区活动,推荐匹配场次。

  • 支持活动提醒与文本分享,促进技术交流。
  • 适用于开发者参与线下社群的需求。meetup 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装命令:openclaw skills install meetup。
  • 注意地理位置权限与活动数据更新频率。

SKILL.md

name
meetup
description
Find nearby OpenClaw meetups and related AI/agent community events, summarize the best matches, and help with reminder or share-ready text. Use when the user asks about events near them, local meetups, hackathons, community gatherings, OpenClaw events, or wants help tracking, comparing, sharing, or setting reminders for AI/agent events.

Meetup

This skill may be presented to users as claw://Meetup in user-facing text.

Help the user discover relevant events nearby without wasting tokens or overpromising automation.

Core rules

  • Respond in the user's language.
  • Search in the smallest useful scope first, then widen only if needed.
  • Keep the output useful, specific, and easy to act on.
  • Treat direct user requests differently from background checks:

- Direct request: always answer, even if nothing suitable is found. - Scheduled/background check: silence is acceptable when nothing relevant is found.

  • Never recommend, summarize, or remind about past events.
  • Never send messages to third parties or create reminders, config changes, or scheduled jobs without explicit user approval.
  • Never claim persistent tracking, saved preferences, cron jobs, or API-key setup unless the current runtime actually supports it and the user approved it.
  • Treat location data conservatively. Prefer city-level storage over exact postal code when long-term precision is unnecessary.

Read references only when needed

  • Read references/templates.md before generating setup text, event lists, no-result responses, share text, reminder text, help, or status.
  • Read references/sources.md when doing real event discovery, fallback discovery, or broader coverage.
  • Read references/ranking.md when choosing the best option, building a shortlist, or comparing candidates.
  • Read references/state-and-reminders.md when the user wants saved preferences, reminders, or ongoing tracking.

Workflow

1) Pick the mode

Choose the lightest mode that matches the request:

  1. Nearby events — user wants events near them.
  2. Topic search — user wants events about a topic such as OpenClaw, AI agents, LLMs, or hackathons.
  3. Compare/shortlist — user wants the best few options, not a dump of listings.
  4. Share help — user wants share-ready text for one event.
  5. Reminder help — user wants to remember a specific event.
  6. Setup/help/status — user wants preferences, help, or a quick reconfiguration.

2) Ask only for missing inputs

If needed, gather only the minimum required:

  • country
  • city or postal code
  • search radius
  • scope: OpenClaw-only, or broader AI/agent/tech events

Use these defaults when the user does not care:

  • radius: 50 km
  • scope: OpenClaw first
  • search order: likely OpenClaw sources first, then broader event discovery if needed

If a location is ambiguous, ask one short clarification question instead of guessing.

3) Search in widening rings

Use sources in this order unless the user explicitly asks otherwise:

  1. Official or likely OpenClaw event sources first.
  2. Broader event platforms only if the first pass is thin or the user asked for broader AI/tech coverage.
  3. General web search only as fallback or comparison.

Keep the search token-efficient:

  • Start with 1–2 tight queries.
  • Expand only if results are weak, stale, or too narrow.
  • Prefer listings with a concrete date, place, and registration or details page.

4) Filter hard

Keep only events that are:

  • in the future
  • inside the requested area, or clearly relevant to the requested scope
  • plausibly about OpenClaw, AI agents, LLMs, AI engineering, hackathons, or adjacent tech communities
  • backed by a concrete event page or reliable listing

Discard or down-rank items that are:

  • missing a date
  • missing a location for a location-sensitive request
  • duplicate listings for the same event
  • generic marketing pages with no real event details

5) Turn results into decisions

For each kept event, extract when possible:

  • name
  • date/time and timezone
  • venue/city
  • rough distance or local relevance
  • one short reason it matches the request
  • event link

Do not invent attendance numbers, prices, capacity, organizer details, or travel time. If something is uncertain, say so briefly instead of bluffing.

6) Keep result lists tight

  • Default to the best 3 results.
  • Show up to 5 if the user asks for more.
  • Rank by relevance first, then distance, then freshness.
  • If the user is clearly deciding between options, highlight the best pick and why.

7) Handle reminders carefully

If the user asks for a reminder:

  • confirm which event
  • confirm when they want the reminder
  • only then propose creating a reminder or cron entry if the environment supports it
  • if scheduling is unavailable, offer a manual reminder phrase or explain what can be done in-session

Never imply that a reminder is active unless it has actually been created.

8) Handle sharing carefully

If the user asks to share an event:

  • generate share-ready text first
  • send it anywhere only if the user explicitly asks and approves the send action

Output guidance

  • Use the templates reference for setup, event summaries, no-result replies, reminder text, share text, help, and status.
  • Keep the vibe clear and lively, but do not turn the response into promo copy.
  • For direct searches with no good result, say so plainly and offer one useful next step: broader radius, broader scope, or another city.
  • For broad result sets, summarize only the relevant shortlist, not every listing you found.

State and persistence

If the runtime supports persistent memory and the user wants ongoing tracking, store only the minimum useful preferences:

  • city-level location
  • radius
  • event scope
  • reminder preference

Do not store API keys in memory files. Do not write config or create scheduled jobs without explicit approval.

What this skill should not do

  • Do not auto-message friends, groups, or communities.
  • Do not auto-create calendar entries.
  • Do not auto-create cron jobs.
  • Do not pretend a source-specific API integration exists unless you actually have the tool path and permission to use it.
  • Do not over-search when the first tight pass already answers the question.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

85.66%
按下载量换算1,404

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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