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-action-itemsgit 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-action-items)<a href="https://agentmods.dev/skills/aashari/ai-agent-skills/mail-action-items"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-action-items/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-action-items"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-action-items.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 | $0.00077 | $0.00993 |
| Opus 5 | $0.00039 | $0.00496 |
| Sonnet 5 | $0.00015 | $0.00199 |
| Haiku 4.5 | $0.00008 | $0.00099 |
Grade A, and why
mail-action-items 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 10d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mail Action Items — Extract Tasks from Email
Time window: $ARGUMENTS (default: last 3 days)
Step 1: Find candidate emails with action signals
Subject-line signals
DB="$HOME/Library/Mail/V10/MailData/Envelope Index"
SINCE=$(($(date +%s) - 259200)) # 3 days default; 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.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 m.date_received >= ${SINCE}
AND m.deleted = 0
AND m.automated_conversation != 2
AND mb.url NOT LIKE '%Spam%' AND mb.url NOT LIKE '%Trash%'
AND mb.url NOT LIKE '%Sent%'
AND (
s.subject LIKE '%action required%'
OR s.subject LIKE '%please review%'
OR s.subject LIKE '%approval%'
OR s.subject LIKE '%approve%'
OR s.subject LIKE '%sign off%'
OR s.subject LIKE '%deadline%'
OR s.subject LIKE '%due%'
OR s.subject LIKE '%RSVP%'
OR s.subject LIKE '%respond by%'
OR s.subject LIKE '%feedback%'
OR s.subject LIKE '%request%'
OR m.is_urgent = 1
OR m.flagged = 1
)
ORDER BY m.is_urgent DESC, m.date_received DESC;" 2>/dev/null
Urgent/flagged unread (always surface)
sqlite3 "$DB" "
SELECT datetime(m.date_received,'unixepoch','localtime') as dt,
s.subject, a.address as sender, m.ROWID
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.read = 0 AND m.deleted = 0
AND m.automated_conversation != 2
AND m.unsubscribe_type = 0
AND mb.url NOT LIKE '%Spam%' AND mb.url NOT LIKE '%Trash%'
AND mb.url NOT LIKE '%Sent%'
ORDER BY m.date_received DESC;" 2>/dev/null
Step 2: Read bodies and extract action items
For each candidate email, parse the body:
python3 ~/.claude/skills/_mail-shared/parser.py <ROWID1> <ROWID2> ...
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.
- 10d ago First seen · 105 lines · 77 tokens per session scan A eec50b5bf1cf
mail-action-items is a skill published in the GitHub repository aashari/ai-agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 77 tokens to every session and 993 once invoked, about $0.0004 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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