google-ads-shopping-campaign-structure-advisor

google-ads-shopping-campaign-structure-advisor is a skill for Claude Code from Ad-Superpowers/ad-superpowers-plugin. It costs 107 tokens per session (3,988 once invoked), scanned A, original, MIT.

A guide to organizing Google Shopping campaigns, which advertise products using information from a product feed. It compares Standard Shopping with Performance Max and covers product groups, campaign priority, and query control.

In plain words
What is it for?
Use it to choose between Shopping campaign types, divide products into groups, plan campaign priorities, and move from Standard Shopping to Performance Max.
Why use it?
Poor campaign structure can mix products with different budgets, margins, or goals and make results harder to manage. It helps choose a structure that fits the store's scale and control needs.

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 choose between Shopping campaign types, divide products into groups, plan campaign priorities, and move from Standard Shopping to Performance Max.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ad-superpowers/ad-superpowers-plugin/google-ads-shopping-campaign-structure-advisor
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-shopping-campaign-structure-advisor
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-shopping-campaign-structure-advisor

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-shopping-campaign-structure-advisor"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-shopping-campaign-structure-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,988 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 $0.00107 $0.03988
Opus 5.5 $0.00043 $0.01595
Sonnet 5.5 $0.00021 $0.00798
Haiku 4.5 $0.00011 $0.00399

Measured 1mo ago against content hash 42275c37fb69, 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-shopping-campaign-structure-advisor 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-shopping-campaign-structure-advisor/SKILL.md · 478 lines

How it starts

The opening of the file, as written. The whole thing — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Shopping Campaign Structure Advisor

Framework for choosing and setting up the optimal Shopping campaign structure. Based on proven structures from the PPC community (SavvyRevenue, Smarter Ecommerce, Optmyzr).

Quick Decision Guide

WHICH SHOPPING STRUCTURE?
==========================

Budget?
|
+---> <$1k/mo
|   +---> PMAX RETAIL ONLY
|       +-- Not enough data for multiple campaigns
|       +-- 1-2 asset groups max
|       +-- Bid strategy: Maximize Conversion Value
|
+---> $1k-5k/mo
|   +---> PMAX RETAIL PRIMARY
|       +-- PMax Retail as main campaign
|       +-- Optional: Standard Shopping catchall (low budget)
|       +-- 2-5 asset groups per category/margin
|       +-- Bid strategy: Target ROAS (if >30 conv/mo)
|
+---> $5k-20k/mo
|   +---> CHOICE:
|       +---> Maximum control -> STANDARD SHOPPING + PMAX (split)
|       |   +-- Standard Shopping for top performers
|       |   +-- PMax for discovery + scale
|       |   +-- Product exclusions to prevent overlap
|       |
|       +---> Maximum scale -> PMAX RETAIL ONLY
|           +-- Multiple campaigns per segment
|           +-- 3-7 asset groups per campaign
|           +-- Custom labels for margin tiers
|
+---> >$20k/mo
    +---> HYBRID STRUCTURE RECOMMENDED
        +-- Standard Shopping for branded + top products
        +-- PMax Retail for non-brand discovery
        +-- Campaign-level budget control
        +-- Advanced listing group filters

7 Shopping Structures That Work

Structure 1: Single PMax Retail (All Products)

WHEN TO USE:
+-- Small product catalog (<50 products)
+-- Budget <$1k/mo
+-- New account without historical data
+-- Maximum simplicity desired

SETUP:
Campaign: PMax Retail - All Products
+-- Budget: $30-50/day
+-- Bidding: Maximize Conversion Value
+-- Asset Group 1: All Products
    +-- Listing Group: UNIT_INCLUDED (everything)
    +-- 15 headlines, 5 descriptions
    +-- 5-10 images (product + lifestyle)
    +-- Audience Signals: Website visitors + In-Market

Read the full file on GitHub · 478 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. 1mo ago First seen · 478 lines · 107 tokens per session scan A 42275c37fb69

Subscribe to this mod's changes

google-ads-shopping-campaign-structure-advisor is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 107 tokens to every session and 3,988 once invoked, about $0.0004 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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