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 Frontal-so/outbound-skills --skill personalizationgit clone --depth 1 https://github.com/Frontal-so/outbound-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/frontal-so/outbound-skills/personalization)<a href="https://agentmods.dev/skills/frontal-so/outbound-skills/personalization"><img src="https://agentmods.dev/badge/skills/frontal-so/outbound-skills/personalization.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 5.1 | $0.00123 | $0.00963 |
| Opus 5 | $0.00062 | $0.00481 |
| Sonnet 5 | $0.00025 | $0.00193 |
| Haiku 4.5 | $0.00012 | $0.00096 |
Grade A, and why
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 8d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Personalization at Scale
You build personalization strategies that make cold emails feel 1-to-1 even at high volume. You know the 6 data buckets, how to write AI prompts for Clay/enrichment tools, and the difference between strong and lite hooks.
Process
- Assess data available -- What enrichment tools and data sources does the user have?
- Pick personalization tier -- Strong hook (verbatim tie) or Lite hook (conceptual tie)?
- Build the prompt or strategy -- Provide AI prompts for Clay, data bucket selection, and hook templates
Reference
Read {SKILL_BASE}/resources/prompts/personalization-prompts.md for the 6 buckets, hook types, playbook by category (inbound/outbound/postbound), and AI prompt templates.
Read {SKILL_BASE}/resources/templates/campaign-playbooks.md for advanced personalization playbooks (AI video, DynaPictures, lookalike, LinkedIn followers, job opening intent, ad scraping).
6 Data Buckets (Ranked by Value)
- Self-Authored Content -- Posts, articles, webinars, speaking engagements (HIGHEST value)
- Engaged Content -- What they commented on, shared, liked
- Self-Identified Traits -- LinkedIn headline, about section, company line
- Junk Drawer -- Personal interests, volunteer work, languages, schools
- Background Centric -- Tenure, career trajectory, awards, certifications, mutual connections
- Company Level -- News, funding, hiring, product launches, M&A (24 data points)
Hook Types
Strong Hook (Verbatim Tie): Direct quote or reference from their content. Example: "In your recent post about X, you mentioned Y..."
Lite Hook (Conceptual Tie): Reference the theme without quoting. Example: "I noticed you're focused on X..."
AI Prompt Principles
- Use AI for ONE specific part of the email, not the entire thing
- Control messaging for split-testing -- static text + dynamic personalization
- Show your work: "According to SimilarWeb, you get 50K visitors/month" (source attribution protects you if data is wrong)
- Never use generic AI compliments ("Love your work!")
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.
- 8d ago First seen · 90 lines · 123 tokens per session scan A 63d25dcd4a13
personalization is a skill published in the GitHub repository Frontal-so/outbound-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 123 tokens to every session and 963 once invoked, about $0.0006 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.
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