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by Sanitysanity-io/agent-toolkit187 stars
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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.

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Use this Skill: https://skilld.dev/gh/sanity-io/agent-toolkit/content-experimentation-best-practices

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SKILL.md

≈104 tokens always: the name and description. ≈360 when used: this file. ≈5.5k more on demand in 4 files.

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

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    The skill provides comprehensive guidelines and best practices for content experimentation and A/B testing. It includes illustrative code snippets for CMS integration and statistical calculation. No security risks were identified.

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    Risk: LOW · No issues

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    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at ad50ea4. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Activeupdated 6 months ago

Topics

  • a-b-testing
  • content-experimentation
  • statistical-analysis
  • cms
  • conversion-optimization
  • multivariate-testing
  • metrics
  • hypothesis-testing

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What it does

Provides guidance on A/B testing, multivariate testing, and statistical analysis for content experiments, including experiment design, metrics selection, sample sizing, CMS integration patterns, and common pitfalls. Use when setting up experimentation infrastructure, designing content variants, or interpreting test results in a headless CMS or frontend stack.

Generated from the current SKILL.md.

Frequently asked

Does this skill cover statistical rigor for A/B tests?
Yes. The skill includes statistical foundations covering p-values, confidence intervals, power analysis, and Bayesian methods to help interpret results correctly.
Can I use this skill to set up experiments in a headless CMS?
Yes. The skill includes guidance on CMS-managed variants and field-level variants, with patterns for integrating experimentation into CMS workflows.
What common mistakes does this skill help avoid?
The skill documents 17 common pitfalls across statistics, design, execution, and interpretation to help teams avoid typical experimentation errors.
Does this cover multivariate testing or just A/B tests?
Both. The skill covers A/B testing, multivariate testing, and how to design experiments that test multiple variables simultaneously.

Generated from the current SKILL.md. These answers refresh after source changes.