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 T4wroot/agentic-seo --skill meta-adsgit clone --depth 1 https://github.com/T4wroot/agentic-seoWrote 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/t4wroot/agentic-seo/meta-ads)<a href="https://agentmods.dev/skills/t4wroot/agentic-seo/meta-ads"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/meta-ads/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/t4wroot/agentic-seo/meta-ads"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/meta-ads.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 | $0.00077 | $0.00917 |
| Opus 5 | $0.00039 | $0.00458 |
| Sonnet 5 | $0.00015 | $0.00183 |
| Haiku 4.5 | $0.00008 | $0.00092 |
Grade A, and why
meta-ads 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 7d 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 meta-ads — 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Ads: Meta Ads
Guides Meta (Facebook/Instagram) Ads setup, campaign structure, audience targeting, and creative optimization. Meta excels at demand generation and visual products; use when creating demand or when creative assets are strong.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Campaign Structure
Hierarchy: Campaign → Ad Set → Ad (3 levels)
Principle: One objective per campaign. Multiple campaigns for the same objective split budget and data, slowing the algorithm's learning phase. Consolidate by objective; focus on clean structure and data-led decisions.
Account
├── Campaign: Prospecting
│ ├── Ad Set: Lookalike 1%
│ └── Ad Set: Broad (Advantage+)
├── Campaign: Retargeting
└── Campaign: Testing
Naming: META_[Objective]_[Audience]_[Offer]_[Date] (e.g., META_Conv_Lookalike-Customers_FreeTrial_2024Q1)
Advantage+ & Automation
| Feature | Use |
|---|---|
| Advantage+ Shopping Campaigns | E-commerce; automatic audience discovery |
| Dynamic Ads | Product catalog; auto-generated creative |
| Automatic Placements | Let Meta optimize across Feed, Stories, Reels |
| Advantage+ Audience | Broad targeting; algorithm finds converters |
Provide diverse creative assets to enable multiple ad formats; algorithm performs better with variety.
Campaign Objectives
| Objective | Use when |
|---|---|
| Awareness | Reach; brand recall |
| Traffic | Clicks to site |
| Conversions | Leads; sales; app installs |
| Engagement | Video views; post engagement |
Audience Targeting
| Type | Best for |
|---|---|
| Lookalikes | Base on best customers (by LTV), not all customers |
| Interest/behavior | Broad; let algorithm optimize |
| Advantage+ | Automated; fewer manual controls |
| Retargeting | Website visitors; engagers; custom audiences |
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.
- 7d ago First seen · 94 lines · 77 tokens per session scan A 28b114d2573e
meta-ads is a skill published in the GitHub repository T4wroot/agentic-seo (15 stars, last pushed 8d ago), licensed MIT. It adds 77 tokens to every session and 917 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to meta-ads, differing in 0 lines, and is treated as a copy.
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