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.
npx agentmods add commands/ad-superpowers/ad-superpowers-plugin/ecommerce-roas-optimizergit clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-pluginWrote 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/commands/ad-superpowers/ad-superpowers-plugin/ecommerce-roas-optimizer)<a href="https://agentmods.dev/commands/ad-superpowers/ad-superpowers-plugin/ecommerce-roas-optimizer"><img src="https://agentmods.dev/badge/commands/ad-superpowers/ad-superpowers-plugin/ecommerce-roas-optimizer.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 | $0.00045 | $0.01336 |
| Opus 5 | $0.00023 | $0.00668 |
| Sonnet 5 | $0.00009 | $0.00267 |
| Haiku 4.5 | $0.00005 | $0.00134 |
Grade A, and why
ecommerce-roas-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 5d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Platforms: meta, google_ads, google_analytics Tier: pro
This command requires the Ad Superpowers MCP connector to access your ad account data. Connect at https://app.adsuperpowers.ai if you haven't already.
E-commerce ROAS Optimizer
Analyze e-commerce ad performance for last 30 days
Conditional: if product_category , focusing on [specify product_category].
OUTPUT FORMAT (CRITICAL - follow this EXACT structure)
REVENUE OVERVIEW
| Metric | Value | vs Prior Period | vs Target |
|---|---|---|---|
| Ad Revenue | |||
| Ad Spend | |||
| Blended ROAS | |||
| Orders from Ads | |||
| AOV |
PRODUCT PERFORMANCE - WINNERS
| Product/Category | Revenue | ROAS | Orders | Platform | Action |
|---|---|---|---|---|---|
| (Sorted by ROAS descending, top 10) |
PRODUCT PERFORMANCE - LOSERS
| Product/Category | Spend | ROAS | Issue | Recommendation |
|---|---|---|---|---|
| (ROAS < 1.5x or below target, sorted by spend descending) |
CATALOG CAMPAIGN BREAKDOWN
| Campaign Type | Platform | Spend | ROAS | Top Products |
|---|---|---|---|---|
| Advantage+ Sales | Meta | |||
| Shopping / PMax |
OPTIMIZATION ACTIONS
- SCALE: [Products with ROAS > target + sufficient volume]
- PAUSE: [Products with ROAS < 1x after 50+ clicks]
- FIX: [Products with high spend but low ROAS - bid/creative/landing page]
BENCHMARKS
Your actual ROAS vs industry:
| Category | Your ROAS | Industry Avg | Status |
|---|
Industry Benchmarks (Reference)
| Category | Typical ROAS |
|---|---|
| Apparel | ~4.5x |
| Garden/Outdoor | ~6.7x |
| Electronics | ~3.2x |
| Beauty | ~5.1x |
| Home & Furniture | ~3.8x |
| Health & Wellness | ~4.2x |
EXECUTION STEPS
Step 1: Discover Accounts
meta_list_ad_accounts()google_ads_list_accounts()ga4_list_properties()
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.
- 5d ago First seen · 120 lines · 45 tokens per session scan A a238f83b5c59
ecommerce-roas-optimizer is a command published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 7d ago), licensed MIT. It adds 45 tokens to every session and 1,336 once invoked, about $0.0002 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 2026-08-31.
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