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 growthenginenowoslawski/coldoutboundskills --skill playbook-first-name-cleaninggit clone --depth 1 https://github.com/growthenginenowoslawski/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/growthenginenowoslawski/coldoutboundskills/playbook-first-name-cleaning)<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-first-name-cleaning"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-first-name-cleaning/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/growthenginenowoslawski/coldoutboundskills/playbook-first-name-cleaning"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-first-name-cleaning.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 | $0.00099 | $0.07535 |
| Opus 5 | $0.00049 | $0.03768 |
| Sonnet 5 | $0.00020 | $0.01507 |
| Haiku 4.5 | $0.00010 | $0.00754 |
Grade A, and why
playbook-first-name-cleaning 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 13d 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 — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook: First Name Cleaning
All rules here are best practice, not law. Override any of them when the campaign calls for it; note the best practice once and move on.
Use when: any campaign whose email opens with the prospect's first name, which is every campaign anyone runs. Run it next to the company-name clean, before any other custom variable, because variables often interpolate the cleaned name.
Do not use when: you need the company name cleaned — that is
playbook-company-name-cleaning, its twin, built to the same shape on purpose. Also not for
finding a missing name. This playbook never looks anything up; a blank stays blank.
One-line output: first_name_clean = "Ruba" from the raw string "Dr Ruba", so the email opens
Hi Ruba, instead of Hi Dr Ruba,.
1. Trigger and scope
The first name is the first word of the email and the single most visible tell that a message was mail-merged. Lead databases store it the way a scraper found it, which is not the way anyone wants to be greeted. Real strings from a live contacts table:
Dr Matthew · PAUL · javonne · 👋 James · ★ Marc · Kathryn (Katie) · Robert wilkie
(last name Wilkie) · Araceli'S · Dr Sean Li We Are Actively Hiring At Antai Global · AAA (at
company AAA Upholstery) · Admin · O. · Gowinder with a trailing space · and rows where the
field is simply NULL.
Every one of those, pasted into Hi {{first_name}},, either looks broken or is not a person.
This playbook takes that string, plus the row's last name and company for context, and returns the short spoken form. It strips honorifics, credential suffixes, emoji and decoration, appended job titles and hiring notices, and possessive artifacts. It fixes shouting and all-lowercase. It picks the nickname when someone wrote one in parentheses. It refuses to mangle hyphenated and apostrophe names.
It explicitly does not: look the person up, translate or transliterate, expand an initial into a guessed name, split a run-together name into two words, or invent a letter not already in the input.
What ships with it
2 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.
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.
- 13d ago First seen · 402 lines · 99 tokens per session scan A c0665ee5229b
playbook-first-name-cleaning is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (702 stars, last pushed 25d ago), licensed MIT. It adds 99 tokens to every session and 7,535 once invoked, about $0.0005 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 2026-08-30.
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