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-fromgit 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-from)<a href="https://agentmods.dev/skills/aashari/ai-agent-skills/mail-from"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-from/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-from"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-from.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 | $0.00076 | $0.00794 |
| Opus 5 | $0.00038 | $0.00397 |
| Sonnet 5 | $0.00015 | $0.00159 |
| Haiku 4.5 | $0.00008 | $0.00079 |
Grade A, and why
mail-from 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mail From — All Emails From a Sender
Sender query: $ARGUMENTS
Steps
1. Find matching senders
DB="$HOME/Library/Mail/V10/MailData/Envelope Index"
QUERY="$ARGUMENTS" # treat as search term against address + comment fields
sqlite3 "$DB" "
SELECT DISTINCT a.address, a.comment, COUNT(*) as cnt
FROM messages m
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE (a.address LIKE '%${QUERY}%' OR a.comment LIKE '%${QUERY}%')
AND m.deleted = 0
GROUP BY a.address
ORDER BY cnt DESC
LIMIT 10;" 2>/dev/null
If multiple matches, show options and ask which one (or proceed with all if they're clearly the same person/org).
2. Get all emails from the matched address(es)
sqlite3 "$DB" "
SELECT datetime(m.date_received,'unixepoch','localtime') as dt,
s.subject, mb.url as mailbox, m.ROWID, m.read, m.flagged
FROM messages m
JOIN subjects s ON m.subject = s.ROWID
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE a.address LIKE '%SENDER%'
AND m.deleted = 0
AND mb.url NOT LIKE '%Spam%'
AND mb.url NOT LIKE '%Trash%'
ORDER BY m.date_received DESC
LIMIT 100;" 2>/dev/null
3. Compute relationship stats
sqlite3 "$DB" "
SELECT
COUNT(*) as total,
SUM(CASE WHEN m.read = 0 THEN 1 ELSE 0 END) as unread,
SUM(CASE WHEN m.flagged = 1 THEN 1 ELSE 0 END) as flagged,
MIN(datetime(m.date_received,'unixepoch','localtime')) as first_email,
MAX(datetime(m.date_received,'unixepoch','localtime')) as latest_email,
strftime('%Y-%m', datetime(m.date_received,'unixepoch','localtime')) as busiest_month
FROM messages m
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE a.address LIKE '%SENDER%' AND m.deleted = 0
GROUP BY busiest_month
ORDER BY COUNT(*) DESC LIMIT 1;" 2>/dev/null
4. Read recent emails if user wants details
python3 ~/.claude/skills/_mail-shared/parser.py <ROWID1> <ROWID2> ...
Output Format
Lead with relationship summary:
- X emails from [name/address], spanning [date range]
- First contact: [date] — Latest: [date]
- Unread: X | Flagged: X
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 · 89 lines · 76 tokens per session scan A d1d422eaa0fc
mail-from is a skill published in the GitHub repository aashari/ai-agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 76 tokens to every session and 794 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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