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+---
+name: content-experimentation-best-practices
+description: Content experimentation and A/B testing guidance covering experiment design, hypotheses, metrics, sample size, statistical foundations, CMS-managed variants, and common analysis pitfalls. Use this skill when planning experiments, setting up variants, choosing success metrics, interpreting statistical results, or building experimentation workflows in a CMS or frontend stack.
+---
+
+# Content Experimentation Best Practices
+
+Principles and patterns for running effective content experiments to improve conversion rates, engagement, and user experience.
+
+## When to Apply
+
+Reference these guidelines when:
+- Setting up A/B or multivariate testing infrastructure
+- Designing experiments for content changes
+- Analyzing and interpreting test results
+- Building CMS integrations for experimentation
+- Deciding what to test and how
+
+## Core Concepts
+
+### A/B Testing
+Comparing two variants (A vs B) to determine which performs better.
+
+### Multivariate Testing
+Testing multiple variables simultaneously to find optimal combinations.
+
+### Statistical Significance
+The confidence level that results aren't due to random chance.
+
+### Experimentation Culture
+Making decisions based on data rather than opinions (HiPPO avoidance).
+
+## References
+
+Start with the reference that matches the current problem, such as design, statistics, CMS integration, or pitfalls. See `references/` for detailed guidance:
+- `references/experiment-design.md` — Hypothesis framework, metrics, sample size, and what to test
+- `references/statistical-foundations.md` — p-values, confidence intervals, power analysis, Bayesian methods
+- `references/cms-integration.md` — CMS-managed variants, field-level variants, external platforms
+- `references/common-pitfalls.md` — 17 common mistakes across statistics, design, execution, and interpretation