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 h4vzz/awesome-ai-agent-skills --skill email-draftinggit clone --depth 1 https://github.com/h4vzz/awesome-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/h4vzz/awesome-ai-agent-skills/email-drafting)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/email-drafting"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/email-drafting/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/h4vzz/awesome-ai-agent-skills/email-drafting"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/email-drafting.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 | $0.00039 | $0.01651 |
| Opus 5.5 | $0.00016 | $0.00660 |
| Sonnet 5.5 | $0.00008 | $0.00330 |
| Haiku 4.5 | $0.00004 | $0.00165 |
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 1mo 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.
This is a copy
97% identical to email-drafting — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Drafting
This skill enables an AI agent to compose professional, context-appropriate emails across a variety of scenarios. The agent analyzes the purpose, audience, and tone requirements, then produces a complete email with subject line, body, and call to action. It supports common email types including cold outreach, follow-up sequences, customer support responses, internal status updates, and meeting requests.
Workflow
-
Identify the email type and objective. Determine the category of the email — cold outreach, follow-up, customer support reply, internal update, or meeting request. Each type has different structural expectations. A cold outreach email prioritizes a compelling hook, while a support response leads with empathy and resolution. Clarify the single desired outcome: a scheduled call, an acknowledged resolution, an informed team, etc.
-
Profile the recipient and calibrate tone. Gather details about the recipient: name, title, company, relationship history, and any previous interactions. Use this context to select the appropriate tone — formal for executive outreach, conversational for peer updates, empathetic for complaint responses, or urgent when time-sensitive action is required. Apply personalization tokens such as
{{first_name}},{{company}}, and{{pain_point}}when drafting templates. -
Compose the subject line. Write a subject line that is specific, concise (under 60 characters), and action-oriented. Avoid generic phrases like "Quick question" or "Touching base." Instead, reference a concrete benefit or context point, e.g., "Cut your onboarding time by 40% — here's how" or "Follow-up: action items from Thursday's standup."
-
Draft the email body with clear structure. Follow a proven structure: opening hook (1-2 sentences establishing relevance), body (2-3 short paragraphs delivering the core message with supporting evidence or context), and closing with a single, unambiguous call to action. Keep paragraphs under 3 sentences. Use bullet points for lists of items or benefits. Avoid walls of text.
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.
- 1mo ago First seen · 120 lines · 39 tokens per session scan A b38a8686463d
email-drafting is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 39 tokens to every session and 1,651 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to email-drafting, differing in 2 lines, and is treated as a copy.
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