Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计异常

meeting-brief会议简报

Agent Skill

meeting-brief 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

1,505

周安装

64

GitHub Stars

1

下载量

527
CodexClaudeCursorGemini CLI

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

unknown

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:meeting-brief(会议简报)
来源仓库:https://github.com/nikiandr/goose-skills
仓库路径:skills/meeting-brief
安装命令:
npx skills add https://github.com/nikiandr/goose-skills --skill meeting-brief
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/nikiandr/goose-skills --skill meeting-brief

简介

meeting-brief 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

  • 它支持通过关键词或任务场景进行信息检索,帮助用户在海量数据中快速筛选出相关结果。
  • 可通过 npx skills add 命令从 GitHub 仓库安装,具体用法建议参考原始 README 文件。
  • 安装前请确认权限范围和维护状态,注意可能涉及联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Meeting Brief

Automated daily meeting preparation system that researches meeting attendees and sends you personalized briefs.

What It Does

Every morning (configurable time):

  1. Checks your calendar for today's meetings (via gcalcli)
  2. Extracts attendees from each meeting
  3. Filters out your team members (configurable)
  4. Deep researches each external person:

- LinkedIn profile (web search) - Company information - GitHub profile (if engineer) - Past interactions/notes (memory search) - Recent news/activity

  1. Generates AI-powered brief per person
  2. Sends 1 email per person to your inbox

Setup

1. Configure Team Members

Edit config.json to list your team members (these will be skipped):

{
  "team_members": [
    "alice@yourcompany.com",
    "bob@yourcompany.com",
    "team@yourcompany.com"
  ],
  "team_domains": [
    "@yourcompany.com"
  ],
  "schedule": "0 7 * * *",
  "timezone": "America/Los_Angeles",
  "your_email": "you@yourcompany.com",
  "brief_from": "Meeting Brief <briefbot@yourcompany.com>",
  "slack_webhook": "https://hooks.slack.com/services/YOUR/WEBHOOK/URL",
  "send_email": true,
  "send_slack": true,
  "include_calendar_details": true,
  "research_depth": "standard"
}

Config options:

  • team_members: Emails to skip (exact match)
  • team_domains: Domain patterns to skip (e.g., skip all @yourcompany.com)
  • schedule: Cron expression for daily run (default: 7am)
  • timezone: Timezone for schedule
  • your_email: Where to send briefs
  • brief_from: From address for briefs
  • slack_webhook: Slack incoming webhook URL (optional)
  • send_email: Whether to send email briefs (default: true)
  • send_slack: Whether to send Slack notifications (default: false)
  • include_calendar_details: Include meeting time/location in brief
  • research_depth: quick (web only), standard (web + GitHub), deep (web + GitHub + past notes)

2. Set Up Daily Cron Job

Use OpenClaw's cron tool to schedule the daily run:

// Create cron job for daily meeting briefs
{
  name: "Meeting Brief - Daily",
  schedule: {
    kind: "cron",
    expr: "0 7 * * *",  // 7 AM daily (UTC)
    tz: "America/Los_Angeles"
  },
  payload: {
    kind: "agentTurn",
    message: "Run the meeting-brief skill for today's meetings",
    timeoutSeconds: 600
  },
  sessionTarget: "isolated"
}

Alternatively, run manually:

cd skills/meeting-brief
./scripts/run_daily.sh

How It Works

Main Workflow (scripts/run_daily.sh)

  1. Fetch today's meetings (scripts/check_calendar.sh)

- Uses gcalcli to get today's agenda - Parses meeting times, titles, attendees - Outputs JSON with meeting details

  1. Filter external attendees (built into run_daily.sh)

- Loads config.json - Filters out team members and team domains - Creates list of people to research

  1. Research each person (scripts/research_person.js)

- Web search: LinkedIn profile, company info - GitHub search: Profile and repos (if applicable) - Memory search: Past interactions/notes - News search: Recent activity - Outputs structured research JSON

  1. Generate brief (scripts/generate_brief.js)

- Uses OpenClaw session to generate AI brief - Inputs: research data + meeting context - Outputs: Two formats: - Email: Concise bullet-point brief - Slack: Rich paragraph-style story with deeper context and narrative

  1. Send brief

- Email: Uses Gmail skill (send_email: true) - Slack: Uses webhook (send_slack: true, scripts/send_slack.sh) - Subject: "Meeting Brief: [Person Name] - [Meeting Title]" - Body: AI-generated brief with research

  1. Save to personal CRM (supernotes/people/)

- Each researched person saved as markdown file - Includes: research data, meeting context, date - Builds personal relationship database over time

  1. Track sent briefs (logs to data/sent/YYYY-MM-DD.json)

- Prevents duplicates - Enables analytics

Research Process

For each external attendee, the system researches:

Web Search (Always)

  • LinkedIn profile (name + company)
  • Company information
  • Recent news mentions
  • Professional background

GitHub (If research_depth is standard or deep)

  • GitHub profile lookup (by name/email)
  • Recent repos and contributions
  • Technical focus areas

Memory/Past Notes (If research_depth is deep)

  • Search MEMORY.md and daily notes
  • Past meeting notes
  • Previous interactions
  • Context from past conversations

Output Format

Research is structured as JSON:

{
  "person": {
    "name": "Jane Doe",
    "email": "jane@example.com",
    "company": "Example Corp",
    "title": "VP Engineering"
  },
  "linkedin": {
    "url": "...",
    "bio": "...",
    "experience": [...]
  },
  "github": {
    "username": "janedoe",
    "profile_url": "...",
    "recent_repos": [...]
  },
  "company": {
    "name": "Example Corp",
    "industry": "...",
    "recent_news": [...]
  },
  "past_interactions": [
    "Met at conference in 2024",
    "Discussed partnership opportunity"
  ]
}

Brief Generation

The AI-generated brief comes in two formats:

Email Format (Concise Bullets)

  1. Quick Overview

- Who they are (name, title, company) - Why you're meeting (meeting title/description)

  1. Background

- Professional background (LinkedIn) - Company context - Technical expertise (GitHub, if applicable)

  1. Conversation Starters

- Based on recent activity - Shared interests/connections - Relevant topics

  1. Action Items / Notes

- Past interactions (if any) - Things to remember - Follow-up items

Slack Format (Rich Story)

Deeper, narrative-driven brief with:

  • Paragraph-style storytelling about the person
  • Context about their journey and recent work
  • Compelling hooks and conversation angles
  • More background color and detail
  • Stronger narrative flow than bullet points

Example Brief:

Subject: Meeting Brief: Jane Doe - Product Partnership Discussion

Hi,

You're meeting with Jane Doe today at 2pm.

## Quick Overview
Jane is VP of Engineering at Example Corp, a B2B SaaS company in the dev tools space. She's been there for 3 years and previously worked at GitHub and Microsoft.

## Background
- Strong background in developer tooling and infrastructure
- Recently led Example Corp's API platform overhaul (launched Q4 2025)
- Active on GitHub (janedoe) - maintains several open-source CLI tools
- Technical blog focuses on API design and developer experience

## Conversation Starters
- Their new API platform (just launched, getting good traction)
- Recent blog post on GraphQL vs REST (published last week)
- Shared interest in developer experience (noted in her LinkedIn)

## Notes
- You met briefly at DevTools Summit 2024
- She mentioned interest in partnering on integration opportunities

---
Meeting: Product Partnership Discussion
Time: Today at 2:00 PM
Location: Zoom (link in calendar)

Manual Usage

Run for a specific person:

# Research a person
node scripts/research_person.js "Jane Doe" "jane@example.com" "Example Corp"

# Generate brief
node scripts/generate_brief.js research_output.json meeting_context.json

# Send brief
./scripts/send_brief.sh brief.html "Jane Doe"

Run for today's meetings:

./scripts/run_daily.sh

Data & Logs

meeting-brief/
├── data/
│   ├── sent/              # Sent brief logs (by date)
│   │   └── 2026-02-21.json
│   ├── research/          # Research cache (by person)
│   │   └── jane-doe.json
│   └── meetings/          # Meeting data (by date)
│       └── 2026-02-21.json
└── logs/
    └── run.log            # Execution logs

Tips

  1. Test with dry-run first: Set DRY_RUN=true in run_daily.sh to preview without sending
  2. Adjust research depth: Start with quick, upgrade to standard or deep as needed
  3. Refine team filter: Add domains/emails to skip internal meetings
  4. Review briefs: Check data/sent/ logs to see what's being sent
  5. Iterate on prompts: Edit generate_brief.js to customize AI prompt

Troubleshooting

No briefs sent:

  • Check gcalcli authentication (gcalcli agenda today tomorrow)
  • Verify calendar has events with external attendees
  • Check logs in logs/run.log

Briefs missing information:

  • Increase research_depth in config.json
  • Check web_search and GitHub CLI are working
  • Review research data in data/research/

Duplicate briefs:

  • Check data/sent/ for already-sent tracking
  • Verify cron job isn't running multiple times

Integration with OpenClaw

This skill uses:

  • gcalcli-calendar: For fetching today's meetings
  • web_search: For LinkedIn and company research
  • GitHub CLI (gh): For GitHub profile lookup
  • memory_search: For past interactions (deep mode)
  • gmail skill: For sending brief emails
  • sessions_spawn: For AI brief generation
  • cron: For daily scheduling

Privacy & Security

  • Research data is cached locally in data/research/
  • No external APIs (uses web_search, GitHub CLI, memory_search)
  • Briefs sent only to configured email
  • Team member filtering prevents leaking internal info
  • All data stored in skill directory (no cloud storage)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

35.14%
按下载量换算185

Claude

28.71%
按下载量换算151

Cursor

21.41%
按下载量换算113

Gemini CLI

10.63%
按下载量换算56

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

可疑

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

继续浏览同类 Skills