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-company-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-company-name-cleaning)<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-company-name-cleaning"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-company-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-company-name-cleaning"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-company-name-cleaning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 234 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00084 | $0.06064 |
| Opus 5.5 | $0.00034 | $0.02426 |
| Sonnet 5.5 | $0.00017 | $0.01213 |
| Haiku 4.5 | $0.00008 | $0.00606 |
Grade A, and why
playbook-company-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 2mo 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook: Company 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.
0. Read this first: the AI prompt is a toggle, not the default
Clay ships a free, deterministic Normalize Company Name action (titleCase: true). That is
the default and it stays the default. Do not replace it reflexively, and do not make AI the
default.
Everything below — the locked prompt, the benchmark, the placeholder guard, the AI column — is the toggle: what you switch on for one client, when the copy leans hard on the company name and the raw strings are messy in ways a normalizer structurally cannot fix. Those are taglines after a pipe, dba entities, parenthetical descriptors, appended city names, second-language duplicates, and junk strings that need an abstain.
Turn the toggle ON when all three are true:
- The copy names the company somewhere a reader will notice (a subject line, the first sentence, a possessive).
- A 20-row read of the normalizer's output on the client's own list finds more than about 2 values you would edit before sending.
- Those failures are the model-shaped kind above, not simple suffix or casing problems. If they are suffix and casing problems, the free normalizer already handles them and the AI column buys you nothing.
Otherwise leave it off. Record which of the two this client is running, so QA reads the right column and nobody re-litigates it mid-campaign.
This deliberately mirrors the opposite decision on first names. First-name cleaning is a standing
column, because a mangled first name breaks the greeting on every campaign that opens with one. A
mangled company name only breaks the campaigns that name the company. See
playbook-first-name-cleaning.
Use when: any campaign whose copy mentions the prospect's company by name, which is nearly all
of them.
Do not use when: you need a company fact — funding, hiring, tech, pricing. Those are separate
playbooks. Also not for deciding whether the company is real or in-ICP.
One-line output: company_clean = "Hamiltons Bud and Bloom" from the raw CRM string
"318, Inc dba Hamiltons Bud and Bloom".
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.
- 2mo ago First seen · 362 lines · 84 tokens per session scan A d33305a887ba
playbook-company-name-cleaning is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (802 stars, last pushed 1mo ago), licensed MIT. It adds 84 tokens to every session and 6,064 once invoked, about $0.0003 per session on Opus 5.5. 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.
playbook-company-name-cleaning compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| playbook-company-name-cleaninggrowthenginenowoslawski/coldoutboundskills | Skill | 802 | 1mo ago | MIT | A | 84 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |