Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/yahav123147-ads-test
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, testing, marketing
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/ads-test
What's inside
- Skill name
ads-test- Description
- A/B test design and experiment planning for paid advertising. Structured hypothesis framework, statistical significance calculator, test duration estimator, sample size calculator, and platform-specific experiment setup guides (Meta Experiments, Google Experiments, LinkedIn A/B). Use when user says A/B test, split test, experiment design, test hypothesis, statistical significance, sample size, or test duration.
- License
- MIT
- Instructions
- 194 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# A/B Test Design & Experiment Planning <!-- Created: 2026-04-13 | v1.5 --> <!-- Source: OpenClaudia/openclaudia-skills (ab-test-setup concept) --> ## Process 1. Understand what the user wants to test (creative, audience, bidding, landing page) 2. Build structured hypothesis using the framework below 3. Calculate required sample size and estimated duration 4. Recommend platform-specific test setup 5. Define success criteria and measurement plan ## Hypothesis Framework Every test must start with a structured hypothesis: ``` IF we [change/action] THEN [metric] will [increase/decrease] by [estimated %] BECAUSE [reasoning based on data or insight] Example: IF we replace polished product shots with UGC creator videos THEN Meta CTR will increase by 25-40% BECAUSE Andromeda prioritizes diverse creative formats and UGC consistently outperforms polished in 2025-2026 benchmarks ``` ### Hypothesis Quality Checklist - [ ] Single variable being tested (isolate the change) - [ ] Specific metric defined (not "performance") - [ ] Estimated effect size stated (needed for sample size calculation) - [ ] Timeframe defined - [ ] Success/failure criteria clear before launch ## Statistical Significance Calculator ``` Required Sample Size (per variant): n = (Z_alpha + Z_beta)^2 × 2 × p × (1-p) / MDE^2 Where: - Z_alpha = 1.96 (for 95% confidence) - Z_beta = 0.84 (for 80% power) - p = baseline conversion rate - MDE = minimum detectable effect (relative %) Simplified lookup: ``` | Baseline CVR | 5% MDE | 10% MDE | 20% MDE | 30% MDE | |-------------|---------|---------|---------|---------| | 1% | 612,000 | 153,000 | 38,300 | 17,000 | | 2% | 302,400 | 75,600 | 18,900 | 8,400 | | 5% | 116,800 | 29,200 | 7,300 | 3,200 | | 10% | 55,200 | 13,800 | 3,450 | 1,530 | | 20% | 24,600 | 6,150 | 1,540 | 680 | *Per variant, 95% confidence, 80% power* ## Test Duration Estimator ``` Duration = Required Sample Size / Daily Traffic per Variant Minimum duration: 7 days (capture weekly patterns) Maximum recommended: 28 days (avoid seasonal drift) Learning phase: Google 7-14 days, Meta 3-7 days, LinkedIn 7-14 days Inputs needed: - Daily impressions or clicks - Number of variants (2 = A/B, 3+ = multivariate) - Baseline conversion rate - Minimum detectable effect desired ``` ### Duration Quick Estimates | Daily Clicks | 2% CVR, 20% MDE | 5% CVR, 20% MDE | 10% CVR, 20% MDE | |-------------|-----------------|-----------------|-----------------| | 100 | 189 days | 73 days | 35 days | | 500 | 38 days | 15 days | 7 days | | 1,000 | 19 days | 7 days | 4 days* | | 5,000 | 4 days* | 2 days* | 1 day* | *Minimum 7 days recommended regardless of sample sufficiency ## Platform-Specific Test Setup ### Meta Experiments - Use Ads Manager > Experiments tab (not manual ad set duplication) - Automatic audience splitting ensures no overlap - Supported test types: A/B (creative, audience, placement), Holdout, Brand Survey - Meta's Incremental Attribution (April 2025) provides AI-powered holdout testing for measuring real causal impact - Budget: split evenly across variants; minimum $100/day per variant recommended - Duration: 7-14 days typical; Meta auto-determines winner at 95% confidence ### Google Experiments - Campaign Experiments (custom experiments) or Ad Variations - Create experiment from existing campaign > select experiment type - Traffic split: 50/50 recommended for fastest results - Supported: bidding strategy, ad copy, landing page, audience - Metrics: choose primary metric (conversions, CPA, ROAS) before launch - Duration: 14-30 days recommended; minimum 2 weeks for bidding tests ### LinkedIn A/B Testing - Built into Campaign Manager for Sponsored Content - Duplicate ad set with single variable change - Target: same audience segment with automatic rotation - Minimum budget: $50/day per variant - Key metrics: CTR (>0.44% benchmark), CPL, Lead Form CVR (13% benchmark) - Duration: 14-21 days (LinkedIn's smaller daily volumes require longer tests) ### TikTok Split Testing - Available in TikTok Ads Manager > Create A/B Test - Test types: targeting, bidding, creative - Auto-splits audience to avoid contamination - Minimum 7 days, recommended 14 days
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Signals
- Stars
- 0
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- May 13, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
ads-test skill. Agent Mart. https://agentmart.dev/skills/yahav123147-ads-test (last updated 2026-10-24)