aashari

mail-triage

Intelligent inbox triage — surface the most important emails across all accounts, prioritized by urgency and requiring attention. Use when user wants a smart overview of what needs their attention, asks "what's important in my email", or wants help deciding what to read first. Arguments: optional time window (default: last 48 hours) or account filter.

aashari 4 Updated 3mo ago
GitHub

Install

npx skillscat add aashari/ai-agent-skills/mail-triage

Install via the SkillsCat registry.

SKILL.md

Mail Triage — Smart Priority Overview

Triaging: $ARGUMENTS (default: last 48 hours)

Step 1: Pull all candidate emails

DB="$HOME/Library/Mail/V10/MailData/Envelope Index"
SINCE=$(($(date +%s) - 172800))  # 48 hours; adjust per $ARGUMENTS

sqlite3 "$DB" "
SELECT datetime(m.date_received,'unixepoch','localtime') as dt,
       s.subject, a.address as sender, a.comment as name,
       mb.url as mailbox, m.ROWID,
       m.read, m.flagged, m.is_urgent, m.size, m.conversation_id,
       m.automated_conversation, m.unsubscribe_type
FROM messages m
JOIN subjects  s  ON m.subject = s.ROWID
JOIN addresses a  ON m.sender  = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE m.date_received >= ${SINCE}
  AND m.deleted = 0
  AND mb.url NOT LIKE '%Spam%' AND mb.url NOT LIKE '%Trash%'
  AND mb.url NOT LIKE '%Junk%' AND mb.url NOT LIKE '%Sent%'
ORDER BY m.date_received DESC;" 2>/dev/null

Step 2: Score each email for priority

High priority signals (+points):

  • is_urgent = 1: +10
  • flagged = 1: +8
  • read = 0: +3
  • Subject contains: urgent, ASAP, action required, critical, deadline, review, approve: +5 each
  • automated_conversation = 0 AND unsubscribe_type = 0 (likely real person): +4
  • From work/EWS account: +2
  • Large message (>50KB): +1 (likely real content)
  • Subject contains reply indicators (RE:, FW:): +2

Low priority signals (−points):

  • automated_conversation = 2 (bulk automated — newsletters, monitoring, CI/CD): −8
  • unsubscribe_type > 0 (has List-Unsubscribe header — mailing list): −5
  • Subject starts with PROBLEM:, OK:, ALARM:, CRIT: −8 (monitoring)
  • Already read: −5

Compute a score per email, then bucket:

  • Score ≥ 10: Critical
  • Score 5–9: Important
  • Score 1–4: Normal
  • Score ≤ 0: Noise

Step 3: Read bodies for top-priority items

For Critical and Important emails (score ≥ 5):

python3 ~/.claude/skills/_mail-shared/parser.py <ROWID1> <ROWID2> ...

Extract key info: what is being asked, any deadlines, any amounts.

Step 4: Check for conversation context

For unread replies, check if it's a continuation of a thread you started:

sqlite3 "$DB" "
SELECT COUNT(*) FROM messages m
JOIN mailboxes mb ON m.mailbox=mb.ROWID
WHERE m.conversation_id=<CONV_ID>
  AND mb.url LIKE '%Sent%';" 2>/dev/null

If you sent something in this thread → higher priority (someone replied to you).

Output Format

Critical — Act Now:
[list with 1-line context from body]

Important — Handle Today:
[list with subject and sender]

Normal — Read When Convenient:
[count + brief list]

Noise (filtered): X emails
[breakdown: Y monitoring alerts, Z newsletters, W CI/CD]

Total: X emails in window. Estimated action time: [estimate based on count].
Offer to read any specific email or start replying.