ab-test-analysis▌
phuryn/pm-skills · updated Apr 8, 2026
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Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.
A/B Test Analysis
Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.
Context
You are analyzing A/B test results for $ARGUMENTS.
If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed.
Instructions
-
Understand the experiment:
- What was the hypothesis?
- What was changed (the variant)?
- What is the primary metric? Any guardrail metrics?
- How long did the test run?
- What is the traffic split?
-
Validate the test setup:
- Sample size: Is the sample large enough for the expected effect size?
- Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE²
- Flag if the test is underpowered (<80% power)
- Duration: Did the test run for at least 1-2 full business cycles?
- Randomization: Any evidence of sample ratio mismatch (SRM)?
- Novelty/primacy effects: Was there enough time to wash out initial behavior changes?
- Sample size: Is the sample large enough for the expected effect size?
-
Calculate statistical significance:
- Conversion rate for control and variant
- Relative lift: (variant - control) / control × 100
- p-value: Using a two-tailed z-test or chi-squared test
- Confidence interval: 95% CI for the difference
- Statistical significance: Is p < 0.05?
- Practical significance: Is the lift meaningful for the business?
If the user provides raw data, generate and run a Python script to calculate these.
-
Check guardrail metrics:
- Did any guardrail metrics (revenue, engagement, page load time) degrade?
- A winning primary metric with degraded guardrails may not be a true win
-
Interpret results:
Outcome Recommendation Significant positive lift, no guardrail issues Ship it — roll out to 100% Significant positive lift, guardrail concerns Investigate — understand trade-offs before shipping Not significant, positive trend Extend the test — need more data or larger effect Not significant, flat Stop the test — no meaningful difference detected Significant negative lift Don't ship — revert to control, analyze why -
Provide the analysis summary:
## A/B Test Results: [Test Name] **Hypothesis**: [What we expected] **Duration**: [X days] | **Sample**: [N control / M variant] | Metric | Control | Variant | Lift | p-value | Significant? | |---|---|---|---|---|---| | [Primary] | X% | Y% | +Z% | 0.0X | Yes/No | | [Guardrail] | ... | ... | ... | ... | ... | **Recommendation**: [Ship / Extend / Stop / Investigate] **Reasoning**: [Why] **Next steps**: [What to do]
Think step by step. Save as markdown. Generate Python scripts for calculations if raw data is provided.
Further Reading
How to use ab-test-analysis on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ab-test-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ab-test-analysis from GitHub repository phuryn/pm-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ab-test-analysis. Access the skill through slash commands (e.g., /ab-test-analysis) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★68 reviews- ★★★★★Kwame Farah· Dec 28, 2024
We added ab-test-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Dhruvi Jain· Dec 24, 2024
Registry listing for ab-test-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mei Smith· Dec 8, 2024
ab-test-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Li Zhang· Dec 8, 2024
Useful defaults in ab-test-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mateo Choi· Dec 8, 2024
Solid pick for teams standardizing on skills: ab-test-analysis is focused, and the summary matches what you get after install.
- ★★★★★Isabella Thompson· Dec 4, 2024
ab-test-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hassan Johnson· Dec 4, 2024
ab-test-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Lucas Abbas· Nov 27, 2024
I recommend ab-test-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Li Liu· Nov 27, 2024
Registry listing for ab-test-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hassan Sharma· Nov 23, 2024
We added ab-test-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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