ab-testing · 스킬 지원 에이전트
문구나 화면 변경을 비교할 실험의 가설, 측정 항목과 진행 조건을 정리합니다.
제목을 바꾸면 가입이 늘지, 버튼 위치를 옮기면 클릭이 늘지 궁금할 때 사용합니다. 무엇을 비교하고 어떤 행동을 개선하려는지 먼저 정해 단순한 취향 비교를 실험으로 바꿉니다.
여러 부분을 동시에 고치기보다 한 가지 가설과 주 지표를 선택하도록 요청하세요. 기존 방문 규모를 알려주면 실행 가능한 실험인지 검토할 수 있습니다. 충분한 표본이 모이기 전에 결과를 확정하지 않도록 종료 조건도 정리합니다.
ab-testing으로 가입 버튼 문구 두 가지를 비교할 실험을 설계해줘. 목표는 가입 완료이고 현재 트래픽은 아래와 같아. 가설, 주 지표, 필요한 데이터와 중단 조건을 알려줘.
받을 수 있는 결과가설·실험 설계·판정 기준
스킬 지원 에이전트와 실제 방문·전환 데이터가 필요합니다. 테스트 배포와 측정 도구 연결은 별도입니다.
현재 사용 중인 스킬 지원 에이전트에 아래 요청을 붙여넣으세요. SKILL.md만 복사하면 보조 파일이 빠질 수 있어요.
https://github.com/coreyhaines31/marketingskills 에서 skills/ab-testing 스킬을 현재 에이전트의 스킬 폴더에 설치해줘. SKILL.md와 이 스킬이 참조하는 scripts·references·assets와 공통 파일을 함께 유지하고, 필요한 실행 환경과 추가 연결을 확인한 뒤 알려줘. 설치가 끝나면 ab-testing 스킬을 인식하는지 확인해줘.
“ab-testing 스킬을 사용해줘. 입력 자료: 변경안·목표 지표·방문 데이터”처럼 요청하세요. 위의 예시를 내 자료에 맞게 바꿔도 좋아요.
예상 결과는 ‘가설·실험 설계·판정 기준’입니다. 필요한 내용이 포함됐는지 확인하세요. 입력 자료의 사실·숫자와 결과를 대조하고, 부족한 부분을 이어서 요청하세요.
제작자 원본 2026-10-04 확인 · 설치 안내 2026-10-04 확인 · 실제 설치·실행 미검증
모델 사용 요금과 연결 앱 요금은 이용 중인 서비스의 정책을 따릅니다.
설치 안내 출처--- name: ab-testing description: When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro. metadata: version: 2.0.0 --- # A/B Test Setup You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results. ## Initial Assessment **Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Before designing a test, understand: 1. **Test Context** - What are you trying to improve? What change are you considering? 2. **Current State** - Baseline conversion rate? Current traffic volume? 3. **Constraints** - Technical complexity? Timeline? Tools available? --- ## Core Principles--- name: ab-testing description: When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro. metadata: version: 2.0.0 --- # A/B Test Setup You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results. ## Initial Assessment **Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Before designing a test, understand: 1. **Test Context** - What are you trying to improve? What change are you considering? 2. **Current State** - Baseline conversion rate? Current traffic volume? 3. **Constraints** - Technical complexity? Timeline? Tools available? --- ## Core Principles ### 1. Start with a Hypothesis - Not just "let's see what happens" - Specific prediction of outcome - Based on reasoning or data ### 2. Test One Thing - Single variable per test - Otherwise you don't know what worked ### 3. Statistical Rigor - Pre-determine sample size - Don't peek and stop early - Commit to the methodology ### 4. Measure What Matters - Primary metric tied to business value - Secondary metrics for context - Guardrail metrics to prevent harm --- ## Hypothesis Framework ### Structure ``` Because [observation/data], we believe [change] will cause [expected outcome] for [audience]. We'll know this is true when [metrics]. ``` ### Example **Weak**: "Changing the button color might increase clicks." **Strong**: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start." --- ## Test Types | Type | Description | Traffic Needed | |------|-------------|----------------| | A/B | Two versions, single change | Moderate | | A/B/n | Multiple variants | Higher | | MVT | Multiple changes in combinations | Very high | | Split URL | Different URLs for variants | Moderate | --- ## Sample Size ### Quick Reference | Baseline | 10% Lift | 20% Lift | 50% Lift | |----------|----------|----------|----------| | 1% | 150k/variant | 39k/variant | 6k/variant | | 3% | 47k/variant | 12k/variant | 2k/variant | | 5% | 27k/variant | 7k/variant | 1.2k/variant | | 10% | 12k/variant | 3k/variant | 550/variant | **Calculators:** - [Evan Miller's](https://www.evanmiller.org/ab-testing/sample-size.html) - [Optimizely's](https://www.optimizely.com/sample-size-calculator/) **For detailed sample size tables and duration calculations**: See [references/sample-size-guide.md](references/sample-size-guide.md) --- ## Metrics Selection ### Primary Metric - Single metric that matters most - Directly tied to hypothesis - What you'll use to call the test ### Secondary Metrics - Support primary metric interpretation - Explain why/how the change worked ### Guardrail Metrics - Things that shouldn't get worse - Stop test if significantly negative ### Example: Pricing Page Test - **Primary**: Plan selection rate - **Secondary**: Time on page, plan distribution - **Guardrail**: Support tickets, refund rate --- ## Designing Variants ### What to Vary | Category | Examples | |----------|----------| | Headlines/Copy | Message angle, value prop, specificity, tone | | Visual Design | Layout, color, images, hierarchy | | CTA | Button copy, size, placement, number | | Content | Information included, order, amount, social proof | ### Best Practices - Single, meaningful change - Bold enough to make a difference - True to the hypothesis --- ## Traffic Allocation | Approach | Split | When to Use | |----------|-------|-------------| | Standard | 50/50 | Default for A/B | | Conservative | 90/10, 80/20 | Limit risk of bad variant | | Ramping | Start small, increase | Technical risk mitigation | **Considerations:** - Consistency: Users see same variant on return - Balanced exposure across time of day/week --- ## Implementation ### Client-Side - JavaScript modifies page after load - Quick to implement, can cause flicker - Tools: PostHog, Optimizely, VWO ### Server-Side - Variant determined before render - No flicker, requires dev work - Tools: PostHog, LaunchDarkly, Split --- ## Running the Test ### Pre-Launch Checklist - [ ] Hypothesis documented - [ ] Primary metric defined - [ ] Sample size calculated - [ ] Variants implemented correctly - [ ] Tracking verified - [ ] QA completed on all variants ### During the Test **DO:** - Monitor for technical issues - Check segment quality - Document external factors **Avoid:** - Peek at results and stop early - Make changes to variants - Add traffic from new sources ### The Peeking Problem Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process. --- ## Analyzing Results ### Statistical Significance - 95% confidence = p-value < 0.05 - Means <5% chance result is random - Not a guarantee—just a threshold ### Analysis Checklist 1. **Reach sample size?** If not, result is preliminary 2. **Statistically significant?** Check confidence intervals 3. **Effect size meaningful?** Compare to MDE, project impact 4. **Secondary metrics consistent?** Support the primary? 5. **Guardrail concerns?** Anything get worse? 6. **Segment differences?** Mobile vs. desktop? New vs. returning? ### Interpreting Results | Result | Conclusion | |--------|------------| | Significant winner | Implement variant | | Significant loser | Keep control, learn why | | No significant difference | Need more traffic or bolder test | | Mixed signals | Dig deeper, maybe segment | --- ## Documentation Document every test with: - Hypothesis - Variants (with screenshots) - Results (sample, metrics, significance) - Decision and learnings **For templates**: See [references/test-templates.md](references/test-templates.md) --- ## Growth Experimentation Program Individual tests are valuable. A continuous experimentation program is a compounding asset. This section covers how to run experiments as an ongoing growth engine, not just one-off tests. ### The Experiment Loop ``` 1. Generate hypotheses (from data, research, competitors, customer feedback) 2. Prioritize with ICE scoring 3. Design and run the test 4. Analyze results with statistical rigor 5. Promote winners to a playbook 6. Generate new hypotheses from learnings → Repeat ``` ### Hypothesis Generation Feed your experiment backlog from multiple sources: | Source | What to Look For | |--------|-----------------| | Analytics | Drop-off points, low-converting pages, underperforming segments | | Customer research | Pain points, confusion, unmet expectations | | Competitor analysis | Features, messaging, or UX patterns they use that you don't | | Support tickets | Recurring questions or complaints about conversion flows | | Heatmaps/recordings | Where users hesitate, rage-click, or abandon | | Past experiments | "Significant loser" tests often reveal new angles to try | ### ICE Prioritization Score each hypothesis 1-10 on three dimensions: | Dimension | Question | |-----------|----------| | **Impact** | If this works, how much will it move the primary metric? | | **Confidence** | How sure are we this will work? (Based on data, not gut.) | | **Ease** | How fast and cheap can we ship and measure this? | **ICE Score** = (Impact + Confidence + Ease) / 3 Run highest-scoring experiments first. Re-score monthly as context changes. ### Experiment Velocity Track your experimentation rate as a leading indicator of growth: | Metric | Target | |--------|--------| | Experiments launched per month | 4-8 for most teams | | Win rate | 20-30% is common for mature programs (sustained higher rates may indicate conservative hypotheses) | | Average test duration | 2-4 weeks | | Backlog depth | 20+ hypotheses queued | | Cumulative lift | Compound gains from all winners | ### The Experiment Playbook When a test wins, don't just implement it — document the pattern: ``` ## [Experiment Name] **Date**: [date] **Hypothesis**: [the hypothesis] **Sample size**: [n per variant] **Result**: [winner/loser/inconclusive] — [primary metric] changed by [X%] (95% CI: [range], p=[value]) **Guardrails**: [any guardrail metrics and their outcomes] **Segment deltas**: [notable differences by device, segment, or cohort] **Why it worked/failed**: [analysis] **Pattern**: [the reusable insight — e.g., "social proof near pricing CTAs increases plan selection"] **Apply to**: [other pages/flows where this pattern might work] **Status**: [implemented / parked / needs follow-up test] ``` Over time, your playbook becomes a library of proven growth patterns specific to your product and audience. ### Experiment Cadence **Weekly (30 min)**: Review running experiments for technical issues and guardrail metrics. Don't call winners early — but do stop tests where guardrails are significantly negative. **Bi-weekly**: Conclude completed experiments. Analyze results, update playbook, launch next experiment from backlog. **Monthly (1 hour)**: Review experiment velocity, win rate, cumulative lift. Replenish hypothesis backlog. Re-prioritize with ICE. **Quarterly**: Audit the playbook. Which patterns have been applied broadly? Which winning patterns haven't been scaled yet? What areas of the funnel are under-tested? --- ## Common Mistakes ### Test Design - Testing too small a change (undetectable) - Testing too many things (can't isolate) - No clear hypothesis ### Execution - Stopping early - Changing things mid-test - Not checking implementation ### Analysis - Ignoring confidence intervals - Cherry-picking segments - Over-interpreting inconclusive results --- ## Task-Specific Questions 1. What's your current conversion rate? 2. How much traffic does this page get? 3. What change are you considering and why? 4. What's the smallest improvement worth detecting? 5. What tools do you have for testing? 6. Have you tested this area before? --- ## Related Skills - **cro**: For generating test ideas based on CRO principles - **analytics**: For setting up test measurement - **copywriting**: For creating variant copy
Original skill: coreyhaines31/marketingskills/skills/ab-testing/SKILL.md Source: https://github.com/coreyhaines31/marketingskills/blob/dda3841f0b294e01e93b1541486beefbfab0915e/skills/ab-testing/SKILL.md License: MIT MIT License Copyright (c) 2025 Corey Haines Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
SKILL.md 본문입니다. 원문에서 참조하는 스크립트·보조 파일은 원본 패키지에 포함되어 있습니다.