Calendar Intelligence

Calendar Intelligence

Transform raw calendar data into actionable patterns and automated workflows. Works with Google Calendar or Microsoft Outlook. Includes LinkedIn-powered attendee due diligence.

Three-phase lifecycle:

1. Install — detect integrations, select calendars, write config
2. Scan & Analyze — pull events, detect patterns, generate report
3. Personalize — recommend automations, user approves, activate agents

Prerequisites

The installing user must have the following connected in their Zo before running:

See references/integrations.md for setup instructions.

Phase 1: Install

Goal: Create data/config.yaml with the user's integrations and preferences.

Interactive Setup (Zo guides the user)

1. Detect available integrations. Check which calendar/email/LinkedIn integrations the user has connected.
2. Ask the user which calendars to scan (if they have multiple).
3. Collect preferences: timezone, work hours, work days, EOD time, delivery method.
4. Run install:

python3 Skills/calendar-intelligence/scripts/install.py init \
  --cal-provider google_calendar \
  --cal-email "user@email.com" \
  --calendars "primary:My Calendar" \
  --mail-provider gmail \
  --mail-email "user@email.com" \
  --linkedin available \
  --timezone "America/New_York" \
  --eod-time "21:00" \
  --delivery email \
  --work-start "09:00" \
  --work-end "18:00" \
  --work-days "1,2,3,4,5"

5. Validate:

python3 Skills/calendar-intelligence/scripts/install.py validate

Config Management

python3 Skills/calendar-intelligence/scripts/install.py status       # Show current config
python3 Skills/calendar-intelligence/scripts/install.py update ...   # Modify fields
python3 Skills/calendar-intelligence/scripts/install.py validate     # Check completeness

Phase 2: Scan & Analyze

Goal: Pull calendar events, normalize them, and run pattern analysis.

Step 2a: Fetch Calendar Events

Use Zo's calendar integration tools to fetch events for the scan window (default: 4 weeks). Save the raw response to a temp file.

For Google Calendar:

use_app_google_calendar("google_calendar-list-events", {
  "calendarId": "<calendar-id>",
  "timeMin": "<4 weeks ago ISO>",
  "timeMax": "<now ISO>",
  "maxResults": 500,
  "singleEvents": true,
  "orderBy": "startTime"
}, email="<user-email>")

Save the response JSON to a temp file, then ingest:

python3 Skills/calendar-intelligence/scripts/scan.py ingest \
  --raw /path/to/raw_response.json \
  --calendar-id "primary"

Repeat for each calendar in the config. The script merges events into a single scan file.

Step 2b: Run Analysis

python3 Skills/calendar-intelligence/scripts/analyze.py run

This generates data/analysis_YYYY-MM-DD.md with:

• 8 pattern analyses (meeting density, buffer gaps, recurring load, collaborator frequency, time clustering, deep work windows, context switching, meeting length distribution)
• Threshold classifications (light/moderate/heavy/etc.)
• Actionable insights per pattern

Quick check:

python3 Skills/calendar-intelligence/scripts/analyze.py quick

Phase 3: Personalize

Goal: Recommend automations based on the user's patterns, let them approve and activate.

Step 3a: Generate Recommendations

python3 Skills/calendar-intelligence/scripts/recommend.py generate

This reads the analysis report and automation templates, and produces data/recommendations_YYYY-MM-DD.yaml with matched recommendations.

Step 3b: Review with User

Present each recommendation and ask for approval:

python3 Skills/calendar-intelligence/scripts/recommend.py list

Step 3c: Activate Approved Automations

For each approved recommendation:

python3 Skills/calendar-intelligence/scripts/recommend.py activate --id <recommendation-id>

This outputs create_agent parameters as JSON. Use them to create the scheduled agent:

create_agent(
    rrule=<rrule from output>,
    instruction=<instruction from output>,
    delivery_method=<delivery from output>
)

EOD Roundup

The EOD Roundup is the flagship automation. When triggered (nightly), Zo should:

1. Fetch Tomorrow's Events

Use the calendar integration to get all events for the next business day. Normalize them into the scan format (or use a fresh scan).

2. Check Attendee Cache

python3 Skills/calendar-intelligence/scripts/eod_roundup.py cache-check \
  --attendees "email1@example.com,email2@example.com"

3. LinkedIn Due Diligence (for uncached/expired attendees)

For each attendee needing refresh, use the LinkedIn integration:

python3 Skills/zo-linkedin/scripts/lk.py search "<attendee name>"
python3 Skills/zo-linkedin/scripts/lk.py profile "<linkedin-public-id>"

Save the profile data and update the cache:

python3 Skills/calendar-intelligence/scripts/eod_roundup.py cache-update \
  --email "attendee@example.com" \
  --profile-json /path/to/profile.json

4. Email Cross-Reference

Use Zo's email integration to search for recent threads with each attendee:

For Gmail:

use_app_gmail("gmail-search-email", {
  "q": "from:<attendee-email> OR to:<attendee-email>",
  "maxResults": 10
}, email="<user-email>")

Compile the email intel into a JSON file with this structure:

{
  "attendees": {
    "email@example.com": {
      "last_contact": "2026-04-12",
      "thread_count": 3,
      "recent_threads": [
        {
          "subject": "Thread subject",
          "date": "2026-04-12",
          "direction": "inbound",
          "snippet": "Preview text"
        }
      ]
    }
  }
}

5. Compile the Digest

python3 Skills/calendar-intelligence/scripts/eod_roundup.py compile \
  --events /path/to/tomorrow_events.json \
  --email-intel /path/to/email_intel.json \
  --linkedin-intel /path/to/linkedin_intel.json

6. Deliver

Send the compiled digest via the user's preferred delivery method (email, SMS, or Telegram).

Available Automation Templates

Script Reference

All scripts support --help for full usage documentation.

Data Files

Adapting This Skill

This skill is designed to be portable. To adapt for a different Zo:

1. Install: Run Phase 1 with the new user's integrations
2. Scan: The analysis adapts to whatever calendar data it finds
3. Personalize: Recommendations are driven by the user's actual patterns
4. Extend: Add new templates to assets/automation_templates.yaml
5. Customize patterns: Edit thresholds in assets/pattern_library.yaml

The data/ directory is gitignored — each installation gets its own data.