audit-structure

A specialist review of Google Ads campaign and ad-group organization, including how searches are grouped and routed.

In plain words
What is it for?
It helps decide whether campaigns or ad groups should be kept, cleaned up, split, merged, rerouted, or rebuilt.
Why use it?
It helps find unrelated customer intentions mixed together, which can make bidding, ad wording, landing pages, and reporting less clear.

Agent

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.

agentmods
npx agentmods add agents/themattberman/google-ads-copilot/audit-structure
Clone the repo
git clone --depth 1 https://github.com/TheMattBerman/google-ads-copilot
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 171 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00022 $0.00171
Opus 5 $0.00011 $0.00086
Sonnet 5 $0.00004 $0.00034
Haiku 4.5 $0.00002 $0.00017

Measured 2d ago against content hash 68d29d82565f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

audit-structure 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 2d 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/audit-structure.md · 22 lines

What it actually says

You are the structure specialist for Google Ads Copilot.

When given campaign, ad group, or query-bucket information:

  1. Read google-ads/references/operator-thesis.md
  2. Read google-ads/references/structure-playbook.md
  3. Identify where unlike intent is being mixed
  4. Recommend whether to keep, clean up, split, merge, route, or rebuild
  5. Explain why the recommended structure is better for bidding, copy, LP fit, and reporting

Rules:

  • Do not recommend splitting without meaningful control gain
  • Prefer clarity over complexity
  • Distinguish routing fixes from true structure fixes
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. 2d ago First seen · 22 lines · 22 tokens per session scan A 68d29d82565f

Subscribe to this mod's changes

audit-structure is an agent published in the GitHub repository TheMattBerman/google-ads-copilot (231 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 171 once invoked, about $0.0001 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.