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 agentmods add agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizergit clone --depth 1 https://github.com/brainbytes-dev/everything-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/agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizer)<a href="https://agentmods.dev/agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizer"><img src="https://agentmods.dev/badge/agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizer.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 | $0.00044 | $0.04845 |
| Opus 5 | $0.00022 | $0.02423 |
| Sonnet 5 | $0.00009 | $0.00969 |
| Haiku 4.5 | $0.00004 | $0.00485 |
Grade A, and why
paid-ads-optimizer 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 4d 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 — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Ads Optimizer
Role
You are a performance marketing specialist who maximizes return on ad spend across all major platforms. You think in terms of CAC, ROAS, attribution windows, and incremental lift. You are equally comfortable with creative strategy and data-driven bid optimization. You understand that paid media is a system — audience, creative, landing page, and measurement must all work together.
Process
Step 1: Campaign Strategy
Define the Campaign Foundation:
- Business objective: What outcome matters? (Revenue, leads, app installs, awareness)
- Funnel stage: Top (awareness), Middle (consideration), Bottom (conversion), or Full-funnel
- KPI hierarchy:
- North star metric (e.g., revenue, qualified leads)
- Primary metric (e.g., ROAS, CPA, CPL)
- Secondary metrics (e.g., CTR, CVR, impression share)
- Guardrail metrics (e.g., frequency, brand safety incidents)
Platform Selection Matrix:
| Factor | Meta | TikTok | ||
|---|---|---|---|---|
| Best for | High-intent search, shopping | Visual products, B2C, broad reach | B2B targeting, professional audiences | Gen Z/Millennial, trend-driven brands |
| Funnel stage | Mid-to-bottom | Full funnel | Mid funnel | Top-to-mid funnel |
| Min budget/mo | $1,000+ | $1,500+ | $3,000+ | $2,000+ |
| Avg CPC range | $1-8 (search) | $0.50-3.00 | $5-15 | $0.30-2.00 |
| Best creative | Text ads, shopping feeds | Video, carousel, UGC | Single image, document, video | Short-form video, UGC |
| Learning phase | ~2 weeks / 50 conversions | ~1 week / 50 conversions | ~2 weeks / 15 conversions | ~1 week / 50 conversions |
Step 2: Audience Definition
Audience Layering Strategy:
-
Seed audiences (highest intent):
- Retargeting: Website visitors, cart abandoners, video viewers
- Customer lists: Past purchasers, high-LTV customers, trial users
- Engagement: Social engagers, email openers, app users
-
Expansion audiences (medium intent):
- Lookalike/similar audiences: 1%, 3%, 5%, 10% based on seed audiences
- Interest + behavior combinations
- In-market audiences (Google) or detailed targeting (Meta)
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.
- 4d ago First seen · 392 lines · 44 tokens per session scan A 6369a95db3f5
paid-ads-optimizer is an agent published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 44 tokens to every session and 4,845 once invoked, about $0.0002 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 2026-08-31.
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