google-ads-performance-max-optimizer

google-ads-performance-max-optimizer is a skill for Claude Code from Ad-Superpowers/ad-superpowers-plugin. It costs 116 tokens per session (3,722 once invoked), scanned A, original, MIT.

A general guide for setting up and improving Google Performance Max campaigns, Google's automated campaigns that can run across several Google channels. It covers campaign structure, audience signals, search themes, assets, and performance by channel.

In plain words
What is it for?
Use it to plan campaigns, choose asset group structures, add audience signals and search themes, and review results by channel or business goal.
Why use it?
It brings the main setup and optimization decisions into one process for both online shops and lead-generation campaigns. It also explains that audience signals guide Google's system but do not strictly limit who sees ads.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ad-superpowers plugin — 120 skills, 35 commands, 5 agents, 1 MCP server shipped together

Good fit Use it to plan campaigns, choose asset group structures, add audience signals and search themes, and review results by channel or business goal.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-optimizer
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.

Any agent
npx skills add Ad-Superpowers/ad-superpowers-plugin --skill google-ads-performance-max-optimizer
Clone the repo
git clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-plugin

Made for: Claude Code.

Or install ad-superpowers, the plugin that ships this one along with the rest of its 120 skills, 35 commands, 5 agents, 1 MCP server.

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 google-ads-performance-max-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-optimizer/github.svg)](https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-optimizer)
Your own site
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-optimizer"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-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 google-ads-performance-max-optimizer

Your own site · 80×15
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-optimizer"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-performance-max-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,722 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 $0.00116 $0.03722
Opus 5.5 $0.00046 $0.01489
Sonnet 5.5 $0.00023 $0.00744
Haiku 4.5 $0.00012 $0.00372

Measured 1mo ago against content hash e5d3c9ab2d00, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

google-ads-performance-max-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 1mo 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.

plugin/skills/google-ads-performance-max-optimizer/SKILL.md · 538 lines

How it starts

The opening of the file, as written. The whole thing — 538 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Max Optimizer

Complete guide for setting up, optimizing and scaling Google Ads Performance Max campaigns for e-commerce and lead generation.

See decision-trees.md for details.

See detailed-reference.md for details.

See detailed-reference.md for details.

Audience Signals

Important Nuance: Signals != Targeting

CRUCIAL CONCEPT
===================

Audience Signals = HINTS for Google AI
Audience Signals != Targeting restrictions

Google WILL advertise outside your signals if it
expects better results. Signals accelerate
the learning phase and provide direction.

Good Signals -> Faster optimization
Bad Signals -> Slower learning, but not a disaster

Audience Signal Strategy

AUDIENCE SIGNAL LAYERING
========================

LAYER 1: First-Party Data (Highest value)
------------------------------------------
[ ] Customer Match
+-- All Customers (conversions)
+-- High-Value Customers (top 20% LTV)
+-- Recent Buyers (30 days)
+-- Lapsed Customers (180+ days)

[ ] Website Visitors
+-- All visitors (180 days)
+-- Product viewers (90 days)
+-- Cart abandoners (30 days)
+-- Converters (exclude or include)

LAYER 2: Google Audiences
--------------------------
[ ] In-Market Segments
+-- Select 3-5 most relevant
+-- Based on: Real purchase intent
+-- Example: "Apparel & Accessories Shoppers"

[ ] Affinity Segments
+-- Broader, more volume
+-- Example: "Fashionistas", "Bargain Hunters"
+-- Less specific, more reach

[ ] Life Events
+-- Very powerful for specific moments
+-- Example: "Recently Moved", "Getting Married"
+-- Smaller audiences, high intent

LAYER 3: Custom Segments
-------------------------
[ ] Search Term Based
+-- People who searched specific terms
+-- Competitor names
+-- Product-specific queries

[ ] Website Based
+-- Visitors of competitor sites
+-- Review sites
+-- Industry publications

Search Themes (2024+ Feature)

Read the full file on GitHub · 538 lines

Files

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.

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. 1mo ago First seen · 538 lines · 116 tokens per session scan A e5d3c9ab2d00

Subscribe to this mod's changes

google-ads-performance-max-optimizer is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 116 tokens to every session and 3,722 once invoked, about $0.0005 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 2026-09-11.

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