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-needs-replygit 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-needs-reply)<a href="https://agentmods.dev/skills/aashari/ai-agent-skills/mail-needs-reply"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-needs-reply/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-needs-reply"><img src="https://agentmods.dev/badge/skills/aashari/ai-agent-skills/mail-needs-reply.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 | $0.00066 | $0.00689 |
| Opus 5 | $0.00033 | $0.00345 |
| Sonnet 5 | $0.00013 | $0.00138 |
| Haiku 4.5 | $0.00007 | $0.00069 |
Grade A, and why
mail-needs-reply scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mail Needs Reply — Unanswered Emails
Looking for: unread emails from humans (not bots) with no follow-up in your Sent.
Step 1: Get unread emails from non-automated senders
DB="$HOME/Library/Mail/V10/MailData/Envelope Index"
# Use SQLite date expressions — never compute Unix epochs manually.
sqlite3 "$DB" "
SELECT datetime(m.date_received,'unixepoch','localtime') as dt,
s.subject, a.address as sender, a.comment as name,
mb.url as mailbox, m.ROWID, m.conversation_id,
m.date_received
FROM messages m
JOIN subjects s ON m.subject = s.ROWID
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE datetime(m.date_received,'unixepoch','localtime') >= date('now','-7 days','localtime')
AND m.read = 0
AND m.deleted = 0
AND m.automated_conversation = 0
AND m.unsubscribe_type = 0
AND mb.url NOT LIKE '%Spam%' AND mb.url NOT LIKE '%Trash%'
AND mb.url NOT LIKE '%Junk%' AND mb.url NOT LIKE '%Sent%'
ORDER BY m.date_received ASC;" 2>/dev/null
Step 2: Check if a reply exists in the same conversation
For each candidate, check if there's a sent message in the same conversation:
sqlite3 "$DB" "
SELECT COUNT(*) FROM messages m
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE m.conversation_id = <CONVERSATION_ID>
AND (mb.url LIKE '%Sent%' OR mb.url LIKE '%sent%')
AND m.date_received > <ORIGINAL_DATE>;" 2>/dev/null
If count > 0, you already replied — skip this email.
Step 3: Age-based prioritization
Group by urgency:
- Overdue (>3 days unread): oldest first — likely forgotten
- Aging (1-3 days): need attention soon
- Recent (<1 day): just came in
Step 4: Read the most overdue ones
python3 ~/.claude/skills/_mail-shared/parser.py <ROWID1> <ROWID2>
Output Format
Emails Waiting for Your Reply
For each:
- Age | From | Subject | [brief context from body if readable]
Group: Overdue → Aging → Recent Total count up front. Note longest-waiting email specifically. Offer to draft a reply to any of them.
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 · 74 lines · 66 tokens per session scan A e88ccdb3302a
mail-needs-reply is a skill published in the GitHub repository aashari/ai-agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 66 tokens to every session and 689 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.
Other skills, from other repositories
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
pinchtab-mcp
Use this skill when a task requires browser automation through PinchTab's MCP server connected to a remote browser instance. Covers navigation, element interaction, data extraction, form filling, multi-step flows, and session management via MCP tools.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
peekaboo
Capture and automate macOS UI with the Peekaboo CLI.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.