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-ad-librarygit 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-ad-library)<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-ad-library"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-ad-library/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-ad-library"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-ad-library.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 | $0.00081 | $0.06267 |
| Opus 5 | $0.00041 | $0.03134 |
| Sonnet 5 | $0.00016 | $0.01253 |
| Haiku 4.5 | $0.00008 | $0.00627 |
Grade A, and why
playbook-ad-library 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 12d 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook: Meta Ad Library
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: the angle depends on the prospect spending money on paid social right now — creative agencies, UGC shops, CRO and landing-page services, media buyers, attribution and analytics tools, or anyone selling to ecommerce brands that already advertise.
Do not use when: you only need to know a company exists or sells online (playbook-tech-on-website),
or you want LinkedIn organic activity (playbook-linkedin-engagement). Facebook and Instagram only —
not Google Ads, not TikTok, not LinkedIn ads.
One-line output: ad_library_line = "you are using ai to run outbound campaigns with one command"
1. Trigger and scope
One question per row: is this company running ads on Meta right now, and what is the ad actually about.
The hard part is not the ad library. It is the input. The Meta Ad Library is keyed to a
Facebook Page; your lists are keyed to a company domain. Nothing maps one to the other for
free. So most of this playbook is a discovery chain that turns a domain into a verified Facebook
Page URL, plus the gates that stop that chain from returning the wrong company's page. Ungated, it
confidently returned a golfer's page for ridge.com and a pottery studio for clay.com.
Two things it deliberately does not do:
- It never reports a raw ad count to a prospect. Meta's
totalCountcounts collated duplicates and overstates reality — one brand showed 950 for what was really about three creatives. - It never guesses. When a page cannot be verified, the row abstains with an empty string rather than shipping a plausible-looking wrong page.
2. Output contract
Inputs required per row
| Field | Type | Source | Required? |
|---|---|---|---|
domain (bare, lowercase, no www, no scheme) |
string | your list | yes |
company_name (cleaned) |
string | your list, cleaned by playbook-company-name-cleaning |
yes — step 2 searches on it |
fb_page_url |
string | this playbook produces it; pass it in to skip to step 4 | no |
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.
- 12d ago First seen · 347 lines · 81 tokens per session scan A 667253da5bd2
playbook-ad-library is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (702 stars, last pushed 25d ago), licensed MIT. It adds 81 tokens to every session and 6,267 once invoked, about $0.0004 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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