Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aashari/ai-agent-skills --skill mail-workgit clone --depth 1 https://github.com/aashari/ai-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aashari/ai-agent-skills/mail-work)<a href="https://agentmods.dev/skills/aashari/ai-agent-skills/mail-work"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-work/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aashari/ai-agent-skills/mail-work"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-work.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.00889 |
| Opus 5 | $0.00030 | $0.00445 |
| Sonnet 5 | $0.00012 | $0.00178 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
mail-work scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mail Work — Work Email Digest
Period: $ARGUMENTS (default: today)
Step 1: Identify work accounts
Work accounts are EWS/Exchange (detected by ews:// prefix in mailbox URL), plus any IMAP account with a corporate domain (not gmail.com, icloud.com, yahoo.com, hotmail.com, outlook.com).
DB="$HOME/Library/Mail/V10/MailData/Envelope Index"
# EWS/Exchange accounts (most reliable work indicator)
EWS_UUIDS=$(sqlite3 "$DB" "
SELECT DISTINCT substr(url, instr(url,'://')+3, instr(substr(url,instr(url,'://')+3),'/')-1)
FROM mailboxes WHERE url LIKE 'ews://%';" 2>/dev/null)
# All mailbox URLs for work accounts
sqlite3 "$DB" "SELECT DISTINCT url FROM mailboxes WHERE url LIKE 'ews://%';" 2>/dev/null
Step 2: Date filter
# Parse $ARGUMENTS
# today: date('now')
# yesterday: date('now','-1 day') to date('now')
# this week: date('now', 'weekday 1', '-7 days') or similar
# default: today
DATE_FILTER="datetime(m.date_received,'unixepoch','localtime') >= '$(date +%Y-%m-%d) 00:00:00'"
Step 3: Query work mail
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
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 ${DATE_FILTER}
AND m.deleted = 0
AND mb.url LIKE 'ews://%'
AND mb.url NOT LIKE '%Spam%'
AND mb.url NOT LIKE '%Trash%'
AND mb.url NOT LIKE '%Junk%'
AND mb.url NOT LIKE '%Draft%'
ORDER BY m.is_urgent DESC, m.read ASC, m.date_received DESC;" 2>/dev/null
Step 4: Read bodies of important emails
For emails that look substantive, prioritize by automated_conversation:
automated_conversation = 0= likely real person → read firstautomated_conversation = 1= transactional (Jira, Slack) → skimautomated_conversation = 2= bulk automated → count only unless urgent
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 85 lines · 60 tokens per session scan A 93974ce983ce
mail-work is a skill published in the GitHub repository aashari/ai-agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 60 tokens to every session and 889 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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