Agent skill
hypothesis_generation
Bilimsel hipotez oluşturma, deney tasarımı ve test metodolojisi rehberi.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/hypothesis-generation-vuralserhat86-antigravity-agentic--2
Metadata
Additional technical details for this skill
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{ "tags": [ "architecture", "automation", "best practices", "clean code", "coding", "collaboration", "compliance", "debugging", "design patterns", "development", "documentation", "efficiency", "git", "hypothesis generation", "optimization", "productivity", "programming", "project management", "quality assurance", "refactoring", "software engineering", "standards", "testing", "utilities", "version control", "workflow" ], "category": "research" }
SKILL.md
🔬 Hypothesis Generation
Bilimsel hipotez oluşturma ve test metodolojisi rehberi.
📋 Hipotez Yapısı
Format
IF [independent variable/action]
THEN [dependent variable/outcome]
BECAUSE [mechanism/reasoning]
Örnek
IF we reduce checkout steps from 5 to 3
THEN conversion rate will increase by 15%
BECAUSE fewer steps reduce friction and drop-off
🎯 Hipotez Kriterleri
| Kriter | Açıklama |
|---|---|
| Specific | Net ve belirsizlik yok |
| Measurable | Ölçülebilir outcome |
| Testable | Test edilebilir |
| Falsifiable | Yanlışlanabilir |
| Relevant | İş hedefine uygun |
🔧 Hipotez Türleri
A/B Test Hipotezi
**Hypothesis:** Changing CTA button from blue to green
will increase click rate by 10%
**Metric:** CTA Click Rate
**Baseline:** 2.5%
**Target:** 2.75%
**Sample Size:** 10,000 users
**Duration:** 2 weeks
Product Hipotezi
**Problem:** Users abandon during onboarding
**Hypothesis:** Adding progress indicator will reduce
abandonment by 20%
**Success Metric:** Onboarding completion rate
📊 Experiment Design
Test Plan
## Experiment: [Name]
### Hypothesis
[IF-THEN-BECAUSE statement]
### Variables
- Independent: [What we change]
- Dependent: [What we measure]
- Control: [What stays same]
### Metrics
- Primary: [Main KPI]
- Secondary: [Supporting metrics]
- Guardrail: [Safety metrics]
### Design
- Type: A/B / Multivariate
- Split: 50/50
- Duration: [X] weeks
### Analysis Plan
- Statistical test: [t-test, chi-square, etc.]
- Confidence level: 95%
- MDE: [Minimum detectable effect]
📝 Prioritization (ICE)
| Hypothesis | Impact | Confidence | Ease | Score |
|---|---|---|---|---|
| H1 | 8 | 7 | 6 | 7.0 |
| H2 | 9 | 5 | 4 | 6.0 |
| H3 | 6 | 8 | 9 | 7.7 |
ICE Score = (Impact + Confidence + Ease) / 3
Hypothesis Generation v1.1 - Enhanced
🔄 Workflow
Aşama 1: Observation
- Data: Analitik verisi veya kullanıcı görüşmesinden bir "Insight" yakala.
- Problem: Gözlemi net bir problem cümlesine dönüştür.
Aşama 2: Construction
- Formula: IF [action] THEN [outcome] BECAUSE [reason] şablonunu kullan.
- Variables: Bağımsız (değişen) ve bağımlı (ölçülen) değişkenleri netleştir.
Aşama 3: Prioritization
- ICE Score: Impact (Etki), Confidence (Güven), Ease (Kolaylık) 1-10 puanla.
- Risk: Test başarısız olursa ne kaybederiz?
Kontrol Noktaları
| Aşama | Doğrulama |
|---|---|
| 1 | Hipotez yanlışlanabilir mi? (Her zaman doğruysa hipotez değildir) |
| 2 | Sonuç ölçülebilir bir metrik mi (Click rate, Retention)? |
| 3 | "Because" kısmı mantıklı bir kullanıcı davranışına dayanıyor mu? |
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