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paid-ads-optimizer

paid-ads-optimizer is an agent for Claude Code from brainbytes-dev/everything-claude-marketing. It costs 44 tokens per session (4,845 once invoked), scanned A, original, MIT.

An assistant for managing paid advertising on Google Ads, Meta, LinkedIn, and TikTok. It considers campaign goals, audiences, budgets, creative work, and performance measurements.

In plain words
What is it for?
Use it to plan campaigns, choose platforms, allocate budgets, evaluate return on ad spend, and improve bids or creative based on results.
Why use it?
It helps organize advertising decisions across platforms instead of judging campaigns by isolated metrics or guesswork.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; positional $N argument.

Part of the everything-claude-marketing plugin — 72 skills, 22 commands, 18 agents, 2 hooks shipped together

not rated 10repo +1 7mo ago A scan 44 tokens original MIT

Good fit Use it to plan campaigns, choose platforms, allocate budgets, evaluate return on ad spend, and improve bids or creative based on results.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizer
View source ↗ brainbytes-dev/everything-claude-marketing
Install

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.

Clone the repo
git clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketing

Made for: Claude Code.

Or install everything-claude-marketing, the plugin that ships this one along with the rest of its 72 skills, 22 commands, 18 agents, 2 hooks.

Wrote 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.

agentmods badge for paid-ads-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizer/github.svg)](https://agentmods.dev/agents/brainbytes-dev/everything-claude-marketing/paid-ads-optimizer)
Your own site
<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/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.

agentmods 80×15 button for paid-ads-optimizer

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,845 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.04845
Opus 5.5 $0.00018 $0.01938
Sonnet 5.5 $0.00009 $0.00969
Haiku 4.5 $0.00004 $0.00485

Measured 2mo ago against content hash 6369a95db3f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

agents/paid-ads-optimizer.md · 392 lines

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.

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:

  1. Business objective: What outcome matters? (Revenue, leads, app installs, awareness)
  2. Funnel stage: Top (awareness), Middle (consideration), Bottom (conversion), or Full-funnel
  3. 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 Google Meta LinkedIn 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:

  1. 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

  2. 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)

Read the full file on GitHub · 392 lines

Changes

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.

  1. 2mo ago First seen · 392 lines · 44 tokens per session scan A 6369a95db3f5

Subscribe to this mod's changes

paid-ads-optimizer is an agent published in the GitHub repository brainbytes-dev/everything-claude-marketing (10 stars, last pushed 7mo ago), licensed MIT. It adds 44 tokens to every session and 4,845 once invoked, about $0.0002 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 2026-08-31.

Analytics and metricsMarketing and SEO
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