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mail-stats

Compute email activity metrics from local mail data by time range and account.

5|Updated Feb 28, 2026
Search Tags:#analytics#email#inbox#statistics#accounts#volume#read-rate
One-click install
npx skills add https://github.com/aashari/ai-agent-skills --skill mail-stats
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mail-stats
Source: https://github.com/aashari/ai-agent-skills/tree/main/skills/apple-mail/mail-stats
Command: npx skills add https://github.com/aashari/ai-agent-skills --skill mail-stats

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps users understand email activity by providing volume trends, peak periods, and read/unread insights across accounts.

Core Features & Use Cases

  • Daily, weekly, and monthly volume breakdown across accounts.
  • Read rate, unread counts, and per-account breakdowns.
  • Use case: plan inbox management and monitor workload over time.

Quick Start

Ask it to generate a 30-day email volume report by day for your primary account.

Frequently Asked Questions about mail-stats

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate email statistics for my inbox?▼

You can generate email statistics by computing volume trends, peak periods, and read/unread insights across accounts from local mail data over daily, weekly, or monthly ranges.

Can I track read rate and unread counts across multiple email accounts?▼

Yes, email analytics can track read rates and unread counts with per-account breakdowns, allowing you to monitor workload and inbox activity across different organizational or personal accounts.

What is the best way to analyze email volume trends over a 30-day period?▼

Analyzing email volume trends over a 30-day period involves selecting a monthly time range and generating a day-by-day volume report for your primary account to identify peak periods.

Does this email analytics approach require any external dependencies?▼

No external dependencies are required; the skill performs deterministic data extraction and time-range selection directly on local mail data to ensure reproducible statistical outputs.

Why should I use deterministic data extraction for inbox analysis?▼

Deterministic data extraction ensures reproducible statistical outputs, meaning your email volume breakdowns and read-rate calculations remain consistent and accurate across multiple analysis runs.

How do I plan inbox management using email activity metrics?▼

Plan inbox management by reviewing email activity metrics like volume trends and read rates to monitor workload over time, helping you identify peak periods and prioritize responses effectively.

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