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 AlexisMarasigan/coldoutboundskills --skill personalization-subagent-patterngit clone --depth 1 https://github.com/AlexisMarasigan/coldoutboundskillsWrote 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/alexismarasigan/coldoutboundskills/personalization-subagent-pattern)<a href="https://agentmods.dev/skills/alexismarasigan/coldoutboundskills/personalization-subagent-pattern"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/personalization-subagent-pattern/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/alexismarasigan/coldoutboundskills/personalization-subagent-pattern"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/personalization-subagent-pattern.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 | $0.00106 | $0.02673 |
| Opus 5 | $0.00053 | $0.01337 |
| Sonnet 5 | $0.00021 | $0.00535 |
| Haiku 4.5 | $0.00011 | $0.00267 |
Grade A, and why
personalization-subagent-pattern 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.
This is a copy
100% identical to personalization-subagent-pattern — 0 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 — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Personalization Sub-Agent Pattern
Cold email personalization at scale requires per-lead generation. Claude Code's Task tool lets you fan out to many sub-agents in parallel, each personalizing a slice of the lead list. This skill defines the reusable approval-loop pattern.
Always Task tool — never an API key
This skill runs entirely inside Claude Code via the Task tool. No Anthropic SDK calls, no OpenAI calls. This is intentional:
- No extra API spend. Uses your Claude Code plan.
- No key management. Works out of the box.
- Parallel by design. Claude Code spawns multiple Task sub-agents in one message, letting 100 leads finish in the time it takes to personalize 10.
At very large scale (1,000+ leads), the tuned prompt can optionally be shipped to the Anthropic API for throughput. But TUNING and normal campaign runs (under 500 leads) always go through the Task tool.
The approval loop (before full fan-out)
Don't personalize 500 leads and then discover the prompt is wrong. Loop first, then scale.
Round 0 — Sample on 1 lead
- Pick one lead from the batch with a rich
company_description. - Show the user: "Here's the company description I'm working with. Based on this, what would you say to personalize?"
- Display what YOU (Claude) would generate for
situation_line,value_line,cta_soft. - Ask: "Does this feel right? Edit it, and I'll re-tune."
Round 1-N — Batch of 10 with approval
- Spawn one Task sub-agent with the current prompt + 10 leads.
- Display all 10 results in a table:
Lead | situation_line | value_line | cta_soft [email protected] | You're building the only APM for Ruby. | Our Ruby customers find ... | Worth 10 min? [email protected] | ... | ... | ... - Ask: "Any edits? Point at the row number and say what's wrong."
- If the user has edits, update the prompt (or add rules like "never use the word X") and re-run a new batch.
- If the user has zero edits for 2 consecutive rounds, the prompt is locked. Scale to the full list.
What ships with it
1 file 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.
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 · 276 lines · 106 tokens per session scan A 0278a8a4a618
personalization-subagent-pattern is a skill published in the GitHub repository AlexisMarasigan/coldoutboundskills (4 stars, last pushed 4mo ago), licensed MIT. It adds 106 tokens to every session and 2,673 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to personalization-subagent-pattern, differing in 0 lines, and is treated as a copy.
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