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 agentmods add skills/ag2ai/ag2-assistant/email-draftingnpx skills add ag2ai/ag2-assistant --skill email-draftinggit clone --depth 1 https://github.com/ag2ai/ag2-assistantWrote 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/ag2ai/ag2-assistant/email-drafting)<a href="https://agentmods.dev/skills/ag2ai/ag2-assistant/email-drafting"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-assistant/email-drafting.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00356 |
| Opus 5 | $0.00019 | $0.00178 |
| Sonnet 5 | $0.00008 | $0.00071 |
| Haiku 4.5 | $0.00004 | $0.00036 |
Grade A, and why
email-drafting 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 5d 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.
What it actually says
Drafting emails & messages
Write a draft the user could send as-is.
Before writing
- Use what you know about the user. Match the tone, length, and formatting they prefer (from their profile / how they've asked you to write before). Some people want warm and chatty; others want three terse lines. Don't default to corporate filler.
- Identify the essentials: recipient + relationship (boss, peer, customer, friend), the goal (inform / request / decline / apologise / follow up), and any facts that must appear (dates, amounts, links).
- If a must-have fact is missing, ask the user rather than inventing it.
Writing
- Open with the point, not throat-clearing. Respect the reader's time.
- One clear ask or takeaway per message; make any action obvious.
- Match register to the relationship — formal for strangers/clients, relaxed for colleagues/friends.
- Suggest a subject line for emails.
- Keep it concise by default; expand only if the user wants detail.
Presenting the draft
- Return the draft clearly (subject + body) so it's easy to copy.
- Offer to adjust tone/length ("want it warmer / shorter / more formal?").
- Don't send anything — you're drafting. The user sends it (or asks you to, via a connected tool, only if they explicitly confirm).
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.
- 5d ago First seen · 38 lines · 39 tokens per session scan A 49fb728033f9
email-drafting is a skill published in the GitHub repository ag2ai/ag2-assistant (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 39 tokens to every session and 356 once invoked, about $0.0002 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 2026-08-30.
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