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 gtmagents/gtm-agents --skill cold-email-personalizationgit clone --depth 1 https://github.com/gtmagents/gtm-agentsWrote 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/gtmagents/gtm-agents/cold-email-personalization)<a href="https://agentmods.dev/skills/gtmagents/gtm-agents/cold-email-personalization"><img src="https://agentmods.dev/badge/skills/gtmagents/gtm-agents/cold-email-personalization/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/gtmagents/gtm-agents/cold-email-personalization"><img src="https://agentmods.dev/badge/skills/gtmagents/gtm-agents/cold-email-personalization.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 | $0.00058 | $0.00810 |
| Opus 5 | $0.00029 | $0.00405 |
| Sonnet 5 | $0.00012 | $0.00162 |
| Haiku 4.5 | $0.00006 | $0.00081 |
Grade A, and why
cold-email-personalization 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cold-email-personalization — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cold Email Personalization
When to Use
- Writing cold outreach, sales prospecting, or outbound B2B emails
- Personalizing email openers from prospect research signals
- Building multi-touch follow-up sequences
- Reviewing or scoring email drafts before sending
Workflow
- Map the ICP — Complete ICP & Objection Mapping. Gate: persona, top 3 objections, and desired next step are defined.
- Research signals — Follow the Research Playbook to find custom signals from the last 90 days. Gate: at least 2 verified signals per prospect.
- Choose campaign path — Pick Custom Signal, Creative Idea, Whole Offer, or Fallback from Campaign Types.
- Draft the email — Apply Email Structure rules: plain text, 60-120 words, one CTA. Use Variable Schema for merge fields.
- QA and score — Run the QA Checklist, then score with the Scoring Rubric. Gate: score >= 80 and 3:1 recipient-to-sender ratio.
- Build sequence — Add follow-ups per Follow-Up Strategy, rotating value props each touch.
Principles
- Message-market fit over cleverness — Show you understand the prospect's situation immediately.
- Research is the personalization — Custom signals prove homework; never fabricate facts.
- One job per email — Single sharp question or CTA; earn a reply, not a meeting.
- Two personalization paths — Path A: custom signal research. Path B: whole-offer strategy. See Creative Ideas for Path B tactics.
Example
Signal found: Prospect's company posted a VP Sales hire on LinkedIn 2 weeks ago.
Subject: {{first_name}}, growing the sales team?
Body: Hi {{first_name}}, saw {{company}} just brought on a new VP Sales — congrats. Specifically, it looks like you're scaling outbound to {{customer_type}}. We helped [similar company] ramp 3 new reps to quota 40% faster by templatizing their top performer's research workflow. Worth a 15-min look?
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/campaign-types.md 1.7 KB
- assets/creative-ideas.md 1.6 KB
- assets/email-structure.md 2.1 KB
- assets/examples.md 3.0 KB
- assets/follow-up-strategy.md 1.6 KB
- assets/icp-objection-mapping.md 2.1 KB
- assets/qa-checklist.md 1.7 KB
- assets/research-playbook.md 2.8 KB
- assets/scoring-rubric.md 1.5 KB
- assets/variable-schema.md 1.4 KB
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 · 52 lines · 58 tokens per session scan A 46705fcb6577
cold-email-personalization is a skill published in the GitHub repository gtmagents/gtm-agents (399 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 58 tokens to every session and 810 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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