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_work_iq (
  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_work_iq (
  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_work_iq (
  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	No	true	Include AI‑generated insights and recommendations

Required MCP Tools

MCP Server	Tool	Purpose
workiq (Local WorkIQ CLI)	`ask_work_iq`	User identity, email retrieval (received and sent), and message metadata for analytics

Tips

Run weekly for a quick health check on your email workload, or monthly for trend spotting.
High automated‑email percentage? Consider unsubscribing or filtering newsletter noise.
Compare week‑over‑week by running twice with different date ranges.
The insights section auto‑detects patterns like automated senders, unread backlogs, and busiest days to give you actionable advice.
If email peaks align with meeting‑heavy days, you may need more focus time.

Examples

Last 7 days at a glance

"Analyze my email patterns for the last week"

Returns a full dashboard covering the past 7 days — total received and sent, top senders, busiest day, unread count, and key insights like automated-email percentage.

Monthly deep-dive

"Give me inbox statistics for March"

Runs a 31-day analysis, identifying volume trends by day of week, ranking your top 10 senders, and flagging any growing unread backlog. Useful for end-of-month reporting or planning the next sprint.

Quick sender check

"Who sends me the most email this quarter?"

Focuses the output on the Top Senders table. Even when requesting a narrow slice, the full dashboard is generated — scroll to the TOP SENDERS section or ask for detail level: summary to limit output to volume and sender rankings only.

Error Handling

No emails found for the requested period If ask_work_iq returns no email results, verify the time period phrasing (e.g., use "last 30 days" instead of a specific date range). The query is natural-language driven — overly precise date strings may not match. Retry with a broader phrase.

Incomplete or partial data If ask_work_iq returns fewer results than expected or omits certain metadata fields, compute analytics on the available data. Append a note to the dashboard: "Some messages could not be fully retrieved and were excluded from analysis."

Large volumes causing slow response For periods longer than 90 days or inboxes with very high traffic (1 000+ messages), a single query may return incomplete results. In this case:

Reduce the analysis window (e.g., analyze one month at a time with separate ask_work_iq calls).
Use detail level: summary to rely on aggregate counts only.

Sent folder unavailable If the sent-mail query returns no results and the user expects sent data, confirm that the account's Sent Items folder is accessible. Set include sent: false to proceed with received-email analytics only.

Identity lookup failure If ask_work_iq cannot resolve the user profile, the dashboard still generates but displays the email address (from email results) instead of the display name. No analytics data is lost.

email-analytics