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.
git clone --depth 1 https://github.com/thatrebeccarae/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/rules/thatrebeccarae/claude-marketing/cross-platform-audit)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/cross-platform-audit"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/cross-platform-audit.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 5.1 | $0.00099 | $0.03454 |
| Opus 5 | $0.00049 | $0.01727 |
| Sonnet 5 | $0.00020 | $0.00691 |
| Haiku 4.5 | $0.00010 | $0.00345 |
Grade A, and why
cross-platform-audit 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 8d 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 — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Platform Audit — Unified Paid Media Assessment
Orchestrates parallel scored audits across Google Ads, Meta Ads, and Microsoft Ads, then merges results into a single budget-weighted health score with cross-platform intelligence.
Context Intake (Always Do First)
Before running audits, collect this information. Without it, benchmarks will be generic and recommendations may be wrong.
Ask these questions (combine into one prompt):
- Business type — E-commerce/DTC, SaaS/B2B, Local Service, Lead Gen, Agency, Other
- Monthly ad spend — Total and per-platform breakdown (approximate is fine)
- Primary goal — Revenue/ROAS, Leads/CPA, App Installs, Brand Awareness
- Active platforms — Which platforms are they advertising on? (Google, Meta, Microsoft, others)
- Data available — What can they provide? (exports, screenshots, pasted metrics, MCP access)
Use the provided context to:
- Determine which platform audits to run (skip platforms they don't use)
- Calculate budget share per platform for aggregate scoring
- Calibrate severity based on spend level ($5K/mo gets different advice than $100K/mo)
Orchestration Workflow
Step 1: Collect Context
Gather business type, platforms, budgets, goals, and available data per the intake above.
Step 2: Spawn Parallel Audits
Use the Task tool to run platform audits simultaneously. Each audit task should:
- Load
skills/shared/scoring-system.mdfor the scoring algorithm - Load the platform-specific
CHECKS.mdfor the audit checklist - Load the platform-specific
SKILL.mdandREFERENCE.mdfor diagnostic context - Evaluate each applicable check as PASS, WARNING, or FAIL
- Calculate the platform health score using weighted formula
- Identify Quick Wins
- Write results to a structured output
Spawn these tasks in parallel (adjust based on which platforms the user has):
Task 1: Google Ads Audit
→ Read skills/shared/scoring-system.md
→ Read skills/google-ads/CHECKS.md
→ Evaluate 74 checks against provided data
→ Calculate score → Write google-audit-results.md
Task 2: Meta Ads Audit
→ Read skills/shared/scoring-system.md
→ Read skills/facebook-ads/CHECKS.md
→ Evaluate 46 checks against provided data
→ Calculate score → Write meta-audit-results.md
Task 3: Microsoft Ads Audit
→ Read skills/shared/scoring-system.md
→ Read skills/microsoft-ads/CHECKS.md
→ Evaluate 30 checks against provided data
→ Calculate score → Write microsoft-audit-results.md
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.
- 8d ago First seen · 316 lines · 99 tokens per session scan A e0df431afb05
cross-platform-audit is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (132 stars, last pushed 3mo ago), licensed MIT. It adds 99 tokens to every session and 3,454 once invoked, about $0.0005 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.
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