/email-analytics

name: email-analytics
description: Analyze your email patterns over a time period — volume trends, top senders, response time estimates, busiest days, and unread backlog statistics.

Email Analytics

Get a data‑driven view of your email life. This skill scans your inbox and sent folder over a time period, crunches the numbers, and presents a formatted analytics dashboard — volume by day, top senders, response patterns, busiest hours, flagged backlogs, and unread trends. Use it to understand your communication load and identify where your email time goes.

When to Use

• "Analyze my email patterns this month"
• "How many emails did I get last week?"
• "Who sends me the most email?"
• "Show me my email volume trends"
• "Give me inbox statistics for the last 30 days"
• "What does my email workload look like?"

Instructions

Step 1: Identify the User

workiq-ask (
  question: "What is my profile information including display name and email address?"
)

Extract **displayName** and **mail** for report personalization.

Step 2: Retrieve Received Emails

Pull all received emails across the analysis period:

workiq-ask (
  question: "List all emails I received in the last <time period>. For each email include the sender name and email, subject, received date and time, read/unread status, importance level, whether it has attachments, and flag status."
)

For longer periods, run multiple queries to capture the full window:

• `"List all emails I received this week with sender, subject, date, read status, importance, attachments, and flag status"`
• `"List all emails I received last week with sender, subject, date, read status, importance, attachments, and flag status"`
• etc.

Step 3: Retrieve Sent Emails

Pull all sent emails for response analysis:

workiq-ask (
  question: "List all emails I sent in the last <time period>. For each email include the recipients, subject, sent date and time, and whether it has attachments."
)

Collect for each message (received and sent):

• **From / To** — sender and recipient addresses
• **ReceivedDateTime** — timestamp for volume trends
• **IsRead** — read vs. unread status
• **Importance** — high/normal/low
• **HasAttachments** — attachment tracking
• **Flag status** — flagged items count
• **Subject** — for thread grouping

Step 4: Compute Analytics

Aggregate the data into the following metrics:

**Volume Metrics:**

• Total received, total sent
• Daily average received / sent
• Ratio of received to sent

**Temporal Patterns:**

• Volume by day of week (Mon–Sun)
• Peak hours (morning, afternoon, evening)
• Busiest single day

**People Metrics:**

• Top 10 senders by volume
• Top 10 recipients you email most
• Emails from direct manager

**Status Metrics:**

• Unread count and percentage
• Flagged count
• High importance count
• Emails with attachments

Step 5: Present the Analytics Dashboard

📊 EMAIL ANALYTICS — {displayName}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📅 Period: {start date} → {end date} ({N} days)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📬 VOLUME OVERVIEW
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  📥 Received:     {total received}    ({daily avg}/day)
  📤 Sent:         {total sent}        ({daily avg}/day)
  📈 Ratio:        {received:sent ratio}
  📬 Unread:       {unread count}      ({unread %}%)
  🚩 Flagged:      {flagged count}
  ⚡ High Priority: {high importance count}

📅 VOLUME BY DAY OF WEEK
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Mon │ ████████████████████░░░░░  82
  Tue │ ███████████████████████░░  94  ← busiest
  Wed │ ██████████████████░░░░░░░  73
  Thu │ █████████████████████░░░░  86
  Fri │ ██████████████░░░░░░░░░░░  58
  Sat │ ███░░░░░░░░░░░░░░░░░░░░░  12
  Sun │ ██░░░░░░░░░░░░░░░░░░░░░░   8

👤 TOP SENDERS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  #1  Firstname1 Lastname1    │ 34 emails │ ████████████████
  #2  notifications@jira     │ 28 emails │ █████████████
  #3  Firstname2 Lastname2   │ 22 emails │ ██████████
  #4  Firstname3 Lastname3   │ 18 emails │ ████████
  #5  build-alerts@ci        │ 15 emails │ ███████
  #6  Firstname4 Lastname4   │ 14 emails │ ██████
  #7  calendar@outlook       │ 12 emails │ █████
  #8  Firstname5 Lastname5   │ 10 emails │ ████
  #9  newsletter@company     │  9 emails │ ████
  #10 help-desk@contoso      │  7 emails │ ███

👤 TOP RECIPIENTS (your sent mail)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  #1  Firstname1 Lastname1    │ 19 emails
  #2  Firstname2 Lastname2   │ 14 emails
  #3  Manager: Firstname6 Lastname6 │ 11 emails
  #4  Firstname3 Lastname3   │  9 emails
  #5  Team DL                │  8 emails

📎 ATTACHMENT STATS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  📎 Emails with attachments: {count} ({percentage}%)
  📊 Most common types:       PDF, XLSX, DOCX

🔍 INSIGHTS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  💡 Tuesday is your busiest email day — consider blocking focus time.
  💡 {unread count} unread emails — consider triaging your inbox.
  💡 {percentage}% of received email is from automated systems.
  💡 You send {ratio} emails for every {N} received.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Output Format

A formatted analytics dashboard with bar charts for volume trends, ranked sender/recipient tables, attachment statistics, and actionable insights. Bar charts use Unicode block characters for visual representation.

Parameters

| Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | Time period | No | Last 7 days | Analysis window (e.g., "this month", "last 30 days", "this quarter") | | Include sent | No | true | Whether to analyze outgoing emails too | | Detail level | No | full | `summary` (volume only) or `full` (all sections) | | Show insights |