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.
git clone --depth 1 https://github.com/thatrebeccarae/claude-marketingWrote 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/rules/thatrebeccarae/claude-marketing/google-ads)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/google-ads"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/google-ads.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.04424 |
| Opus 5 | $0.00025 | $0.02212 |
| Sonnet 5 | $0.00010 | $0.00885 |
| Haiku 4.5 | $0.00005 | $0.00442 |
Grade A, and why
google-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.
How it starts
The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Ads
Expert-level guidance for Google Ads — auditing, building, and optimizing search, shopping, display, video, Performance Max, and demand gen campaigns.
Core Capabilities
Campaign Auditing & Optimization
- Full account audit covering structure, settings, bidding, keywords, ads, and tracking
- Quality Score optimization (expected CTR, ad relevance, landing page experience)
- Search term analysis and negative keyword management
- Budget allocation and pacing optimization
- Wasted spend identification and elimination
Campaign Types
- Search — Text ads on Google Search and Search Partners
- Shopping — Product Listing Ads from Google Merchant Center
- Performance Max — AI-driven cross-channel (Search, Shopping, Display, YouTube, Discover, Gmail, Maps)
- Display — Banner/responsive ads across Google Display Network (3M+ sites)
- Video — YouTube ads (in-stream, bumper, discovery, shorts)
- Demand Gen — Visual-first ads on YouTube, Discover, Gmail
- App — App install and engagement campaigns
Bidding Strategies
- Manual CPC / Enhanced CPC — Direct control, good for learning phases
- Target CPA — Optimize for cost per conversion
- Target ROAS — Optimize for return on ad spend
- Maximize Conversions / Maximize Conversion Value — Spend full budget optimally
- Target Impression Share — Competitive visibility
- Portfolio bid strategies for cross-campaign optimization
Audience Strategy
- First-party data: Customer Match, website visitors, app users
- Google audiences: In-market, affinity, detailed demographics, life events
- Custom audiences (by keywords, URLs, apps)
- Remarketing lists for Search Ads (RLSA)
- Optimized targeting vs observation mode
- Audience signals for Performance Max
Tracking & Measurement
- Google Tag (gtag.js) / Google Tag Manager implementation
- Conversion actions: website, phone calls, app, import, store visits
- Enhanced conversions (first-party data matching)
- Offline conversion import (GCLID / enhanced conversions for leads)
- Google Ads Data Hub and GA4 integration
- Attribution models: data-driven (default), last-click, cross-channel
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 · 462 lines · 50 tokens per session scan A dedef062bcce
google-ads is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (131 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 4,424 once invoked, about $0.0003 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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