Borrowing it
Nothing to install: this file belongs to Pauesome/Paid-Media-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Pauesome/Paid-Media-MCP/main/.claude/skills/ab-test-design/SKILL.mdgit clone --depth 1 https://github.com/Pauesome/Paid-Media-MCPWrote 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/pauesome/paid-media-mcp/ab-test-design)<a href="https://agentmods.dev/skills/pauesome/paid-media-mcp/ab-test-design"><img src="https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/ab-test-design/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/pauesome/paid-media-mcp/ab-test-design"><img src="https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/ab-test-design.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 | $0.00081 | $0.01165 |
| Opus 5 | $0.00041 | $0.00583 |
| Sonnet 5 | $0.00016 | $0.00233 |
| Haiku 4.5 | $0.00008 | $0.00117 |
Grade A, and why
ab-test-design 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 11d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A/B Test Design — Spain
Plans rigorous paid-ad experiments. Thresholds and sample-size lookup come from
.claude/references/benchmarks-spain.md.
Required Inputs
Up-front (no mid-run prompts):
- Platform (google_ads / meta_ads / tiktok_ads)
- What is being tested (creative concept, hook, audience, bidding strategy, landing page, etc.)
- Baseline CVR (if not known, pull from MCP using client_id + 30-day window)
- Minimum Detectable Effect (MDE) — relative %
- Daily traffic or click volume per variant (pull from MCP if client_id given)
Hypothesis Framework
Every test starts with a structured hypothesis. Enforce this format:
IF we [change / action]
THEN [metric] will [increase / decrease] by [estimated %]
BECAUSE [reasoning based on prior data or insight]
Hypothesis quality checklist:
- Single variable isolated
- Specific metric defined (not "performance")
- Effect size stated (required for sample size)
- Timeframe defined
- Success / failure criteria fixed before launch
Sample Size
n_per_variant = (Z_α + Z_β)² × 2 × p × (1 - p) / MDE²
Z_α = 1.96 (95% confidence)
Z_β = 0.84 (80% power)
p = baseline conversion rate
MDE = relative detectable effect
For quick estimates, use the lookup table in benchmarks-spain.md
(Testing & Statistical Significance section).
Duration
duration_days = n_per_variant / daily_traffic_per_variant
- Minimum 7 days (capture weekly patterns)
- Maximum 28 days (avoid seasonal drift)
- Respect platform learning-phase floors: Google 7–14 d, Meta 3–7 d, TikTok 7–14 d
If the calculated duration < 7 days, still run ≥ 7 days. If > 28 days, either increase MDE, increase traffic, or skip the test.
Platform Setup
Meta Experiments
- Ads Manager → Experiments (not manual duplication)
- Automatic audience splitting
- Budget: ≥ €75/day per variant
- Duration 7–14 d; Meta auto-declares winner at 95% confidence
Google Experiments
- Campaign experiments or Ad Variations
- 50/50 traffic split
- Primary metric fixed before launch (conversions / CPA / ROAS)
- Duration 14–30 d; minimum 2 weeks for bidding tests
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.
- 11d ago First seen · 163 lines · 81 tokens per session scan A ba8eaf6ad238
ab-test-design is a skill published in the GitHub repository Pauesome/Paid-Media-MCP (1 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 1,165 once invoked, about $0.0004 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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