Analyze A/B test results with statistical significance, practical significance, and segmentation analysis to produce ship/no-ship recommendations supported by statistical justification.
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npx claudepluginhub tonone-ai/tonone --plugin eval-analyzeA/B test design — produce an experiment spec with hypothesis, primary metric, MDE, sample size, run time, and decision rule. Also determines when NOT to A/B test and what to do instead. Use when asked to "design an A/B test", "should we test this", "experiment design", "how do we know if this works", "what's the sample size", or "set up an experiment".
tonone-aiPROACTIVELY use this agent when experiments are started, modified, or when results need analysis. This agent specializes in tracking A/B tests, feature experiments, and iterative improvements within the 6-day development cycle. Should be triggered automatically when experimental code paths or feature flags are introduced. Examples:\n\n<example>\nContext: When implementing feature flags or A/B test variants\nuser: "Add a feature flag to test the new onboarding flow"\nassistant: "I've implemented the feature flag for the onboarding flow A/B test. Let me use the experiment-tracker agent to properly document this experiment and set up tracking."\n<commentary>\nEvery feature flag represents an experiment that needs proper tracking and success metrics.\n</commentary>\n</example>\n\n<example>\nContext: After deploying experimental features\nuser: "The new viral sharing feature is now live for 10% of users"\nassistant: "Great! The viral sharing feature is deployed. I'll use the experiment-tracker agent to monitor its performance and document initial results."\n<commentary>\nLive experiments need immediate tracking setup to capture valuable data from the start.\n</commentary>\n</example>\n\n<example>\nContext: When reaching experiment milestones\nuser: "It's been a week since we launched the TikTok integration test"\nassistant: "Perfect timing for analysis. Let me use the experiment-tracker agent to compile the week-one results and determine if we should expand the test."\n<commentary>\nRegular experiment checkpoints ensure data-driven decisions about feature rollouts.\n</commentary>\n</example>\n\n<example>\nContext: Before making product decisions\nuser: "Should we keep the AI avatar feature or remove it?"\nassistant: "Let me analyze the experiment data to inform this decision. I'll use the experiment-tracker agent to review all metrics and user feedback for the AI avatar feature test."\n<commentary>\nProduct decisions should be backed by experiment data, not gut feelings.\n</commentary>\n</example>
devsforgeData & analytics skills: Metrics Framework, SQL Query Explainer, Dashboard Brief, Cohort Analysis, Data Pipeline Spec, Chart Data Extractor, A/B Test Readout, Metric Tree Builder, Data Quality Audit. Build North Star metric trees, explain and optimise SQL, spec dashboards, read out A/B test results with significance and guardrails, and audit datasets for quality before you trust them.
mohitagw15856Use this agent when analyzing metrics, generating insights from data, creating performance reports, or making data-driven recommendations. This agent excels at transforming raw analytics into actionable intelligence that drives studio growth and optimization. Examples:\n\n<example>\nContext: Monthly performance review needed
ccpluginsMetrics, experimentation, and data-informed product decisions.
sethdfordAmplitude-powered analytics skills — analyze dashboards, charts, experiments, feedback, and account health with AI.
amplitudeDesign and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
tonone-aiGenerate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
tonone-aiImplement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
tonone-aiVerify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
tonone-aiML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
tonone-ai