Structured guidance for designing, executing, and analyzing content experiments to improve conversion and engagement.
Works with
Covers hypothesis frameworks, metric selection, sample size calculation, and statistical significance testing across A/B and multivariate experiments
Includes detailed resources on p-values, confidence intervals, power analysis, and Bayesian methods for interpreting results
Provides CMS integration patterns for managing variants at the field level and connecting exter
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncontent-experimentation-best-practicesExecute the skills CLI command in your project's root directory to begin installation:
Fetches content-experimentation-best-practices from sanity-io/agent-toolkit and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate content-experimentation-best-practices. Access via /content-experimentation-best-practices in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Principles and patterns for running effective content experiments to improve conversion rates, engagement, and user experience.
Reference these guidelines when:
Comparing two variants (A vs B) to determine which performs better.
Testing multiple variables simultaneously to find optimal combinations.
The confidence level that results aren't due to random chance.
Making decisions based on data rather than opinions (HiPPO avoidance).
Start with the resource that matches the current problem, such as design, statistics, CMS integration, or pitfalls. See resources/ for detailed guidance:
resources/experiment-design.md — Hypothesis framework, metrics, sample size, and what to testresources/statistical-foundations.md — p-values, confidence intervals, power analysis, Bayesian methodsresources/cms-integration.md — CMS-managed variants, field-level variants, external platformsresources/common-pitfalls.md — 17 common mistakes across statistics, design, execution, and interpretationPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Solid pick for teams standardizing on skills: content-experimentation-best-practices is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: content-experimentation-best-practices is focused, and the summary matches what you get after install.
Registry listing for content-experimentation-best-practices matched our evaluation — installs cleanly and behaves as described in the markdown.
content-experimentation-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.
content-experimentation-best-practices reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: content-experimentation-best-practices is focused, and the summary matches what you get after install.
content-experimentation-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.
content-experimentation-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: content-experimentation-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: content-experimentation-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.
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