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
5. Generates AI-powered brief per person
6. 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": [
    "[email protected]",
    "[email protected]",
    "[email protected]"
  ],
  "team_domains": [
    "@yourcompany.com"
  ],
  "schedule": "0 7 * * *",
  "timezone": "America/Los_Angeles",
  "your_email": "[email protected]",
  "brief_from": "Meeting Brief <[email protected]>",
  "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. Run Daily (Manual or Scheduled)

Run manually each morning:

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

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

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

3. 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

4. Generate brief (scripts/generate_brief.js)

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

5. 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

6. 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

7. 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": "[email protected]",
    "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)

2. Background

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

3. Conversation Starters

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

4. 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" "[email protected]" "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

Tools Used

• gcalcli: For fetching today's meetings from Google Calendar
• web_search: For LinkedIn and company research
• GitHub CLI (gh): For GitHub profile lookup (optional)
• Gmail: For sending brief emails (optional — can also output to file)

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)