ab-test-analysis
SkillProductivityLets your agent analyze A/B test results for statistical significance and recommend whether to ship, extend, or stop a variant.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ab-test-analysis skill
About this capability
Use when a task needs analysis of A/B test results, interpretation of p-values and confidence intervals, statistical significance checks, or a principled ship/no-ship decision.
What this skill tells your AI
The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/ab-test-analysis/SKILL.md and read by ahel’s review.
Instructions
Own A/B test analysis as a principled ship/no-ship decision, not a p-value lookup.
Separate statistical significance from practical significance, and catch common analysis traps before recommending action.
Working mode:
- Establish the experiment design: primary metric, guardrails, pre-specified minimum detectable effect, and duration.
- Validate test integrity (sample ratio match, sufficient power, no peeking) before trusting any result.
- Quantify the effect: observed lift, confidence interval, p-value, and whether the result is practically meaningful.
- Issue a ship / no-ship / iterate / inconclusive verdict with explicit rationale.
Focus on:
- p-value meaning: probability of data this extreme under the null, not the probability the variant is better
- effect size and confidence interval as the decision drivers, not significance alone
- guardrail metrics: never ship a primary win that significantly harms a guardrail
- pre-specified segments only; flag post-hoc segmentation as p-hacking
- common errors: peeking, multiple comparisons, Simpson's paradox, survivorship bias
- frequentist vs bayesian framing and which the tooling actually used
- power and sample adequacy when results are trending but underpowered
Quality checks:
- confirm the verdict aligns with primary metric, effect size, and guardrail status together
- verify no sample ratio mismatch invalidates the test
- check the test ran to its predetermined sample size and duration
- ensure each segment claim was pre-planned, not mined after the fact
- distinguish "no effect detected" from "underpowered, extend the test"
Return:
- results summary table (control vs treatment: n, conversion rate, lift, CI, p-value)
- statistical significance verdict and effect-size interpretation
- guardrail metric status
- ship / no-ship / iterate / inconclusive recommendation with rationale
- next step (ship plan, extension, or learning to capture)
Do not declare a win on statistical significance alone when practical significance or guardrails are unmet unless explicitly requested by the parent agent.
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
ab-test-analysis- Source
- github.com/jshsakura/awesome-opencode-skills