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 skills add Ad-Superpowers/ad-superpowers-plugin --skill incrementality-testing-guidegit 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/skills/ad-superpowers/ad-superpowers-plugin/incrementality-testing-guide)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/incrementality-testing-guide"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/incrementality-testing-guide/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.
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/incrementality-testing-guide"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/incrementality-testing-guide.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00098 | $0.03116 |
| Opus 5.5 | $0.00039 | $0.01246 |
| Sonnet 5.5 | $0.00020 | $0.00623 |
| Haiku 4.5 | $0.00010 | $0.00312 |
Grade A, and why
incrementality-testing-guide 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.
How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incrementality Testing Guide
Purpose
Help advertisers understand and implement incrementality testing to measure the true causal impact of their advertising, beyond what traditional attribution can tell them.
When to Use This Skill
Invoke when user mentions:
- Incrementality: "Is my advertising actually working?"
- Testing: "How do I run a geo lift test?"
- Attribution skepticism: "I don't trust platform reporting"
- True ROAS: "What's my incremental ROAS?"
- Holdout tests: "Should I run a holdout experiment?"
- MMM/Econometrics: "What is marketing mix modeling?"
- Conversion lift: "How do I set up a conversion lift study?"
Part 1: Why Incrementality Testing Matters
The Attribution Problem
Traditional attribution (last-click, platform-reported) has fundamental flaws:
| Attribution Type | What It Measures | What It Misses |
|---|---|---|
| Last-click (GA4) | Final touchpoint | All assist interactions |
| Platform reporting | Platform's attributed conversions | Over-credits, includes organic |
| View-through | Ad view → conversion | Many would convert anyway |
| Multi-touch | Weighted path credit | Still doesn't prove causation |
The Core Problem: Attribution tells you WHO converted after seeing an ad, not WHETHER the ad caused the conversion.
What Incrementality Testing Answers
The Question: "What additional conversions/revenue occurred BECAUSE of advertising that would NOT have happened without it?"
| Metric | Definition | Formula |
|---|---|---|
| Incremental conversions | Conversions caused by ads | Test group - Control group |
| Incrementality rate | % of conversions caused by ads | (Test - Control) / Test |
| Incremental ROAS (iROAS) | True return on ad spend | Incremental Revenue / Ad Spend |
Industry Benchmarks: Typical Incrementality Rates
| Channel/Tactic | Typical Incrementality | Notes |
|---|---|---|
| Prospecting (cold audience) | 70-90% | Reaching new users |
| Generic search | 75-95% | High intent, new customers |
| Brand search | 10-40% | Users already looking for you |
| Retargeting (cart abandoners) | 40-60% | Some would return anyway |
| Retargeting (site visitors) | 20-40% | Higher cannibalization |
| Retargeting (past buyers) | 15-30% | Lowest incrementality |
| Social awareness | 50-80% | Hard to measure directly |
| Display prospecting | 60-85% | Depends on targeting |
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.
- 1mo ago First seen · 379 lines · 98 tokens per session scan A 6d87cfcad923
incrementality-testing-guide is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 98 tokens to every session and 3,116 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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