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sanity-io/agent-toolkit2.3k installs

content-experimentation-best-practices

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.

How do I install this agent skill?

npx skills add https://github.com/sanity-io/agent-toolkit --skill content-experimentation-best-practices
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Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill provides educational content and code templates for designing and implementing content experimentation and A/B testing workflows. It includes statistical guidance, CMS integration patterns specifically for Sanity.io, and common pitfalls to avoid. No security risks or malicious patterns were identified.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • Runlayerpass

    5 files scanned · No issues

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

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

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

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