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 seb1n/awesome-ai-agent-skills --skill sales-email-sequencesgit clone --depth 1 https://github.com/seb1n/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/seb1n/awesome-ai-agent-skills/sales-email-sequences)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/sales-email-sequences"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/sales-email-sequences/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/seb1n/awesome-ai-agent-skills/sales-email-sequences"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/sales-email-sequences.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.01878 |
| Opus 5 | $0.00028 | $0.00939 |
| Sonnet 5 | $0.00011 | $0.00376 |
| Haiku 4.5 | $0.00006 | $0.00188 |
Grade A, and why
sales-email-sequences 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sales Email Sequences
Design end-to-end outbound email sequences that move prospects from cold outreach to booked meetings. This skill builds persona-targeted messaging across multiple touches — intros, follow-ups, value-adds, and breakup emails — with personalization tokens, subject line variants, and send-timing cadences optimized for reply rates.
Workflow
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Define ICP and Persona — Establish the ideal customer profile (industry, company size, revenue range, geography) and the target persona (title, seniority, responsibilities, pain points). This determines tone, vocabulary, value framing, and which proof points resonate.
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Craft the Core Value Proposition — Distill your product's relevance to this persona into a single compelling statement. Focus on a specific, measurable outcome (e.g., "reduce month-end close from 10 days to 3") rather than feature lists. This value prop threads through every email in the sequence.
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Write the Email Sequence — Build a multi-touch sequence: an opening email that earns attention with a relevant hook, follow-ups that introduce new angles or proof points, a value-add email offering a resource, and a breakup email that creates urgency through finality. Each email should be 50–120 words in the body.
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Add Personalization Tokens — Insert dynamic fields for prospect name, company, industry, recent trigger events (funding rounds, job changes, earnings calls), and any known tech stack details. Personalization beyond
{{first_name}}dramatically lifts reply rates. -
Set Timing and Cadence — Define send days, times, and intervals between touches. B2B sequences typically perform best with Tuesday–Thursday sends between 8–10 AM local time, with 2–4 day gaps between early touches and longer gaps (5–7 days) before the breakup.
Usage
Specify the target persona, your product/service, the core pain point you solve, and desired sequence length. Optionally include trigger events or specific personalization data.
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 · 184 lines · 55 tokens per session scan A 4078c3967100
sales-email-sequences is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 1,878 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 2026-09-03.
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