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Found 63 Skills
Amazon Bedrock Prompt Management for creating, versioning, and managing prompt templates with variables, multi-variant A/B testing, and flow integration. Use when creating reusable prompt templates, managing prompt versions, implementing A/B testing for prompts, integrating prompts with Bedrock Flows, optimizing prompt engineering, or building production prompt catalogs.
A/B test subject line variations using proven copywriting frameworks. Predict open rates based on historical performance.
Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions.
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
When the user wants to optimize free trial conversion -- including trial length, trial type selection, expiry flows, or trial email sequences. Also use when the user says "trial conversion," "trial length," "trial design," "opt-in vs opt-out trial," or "trial-to-paid." For activation, see activation-metrics. For feature gating, see feature-gating.
Implement a feature flag system for gradual rollouts, A/B testing, and kill switches. Use when you need to control feature availability without deployments, test features with specific users, or implement percentage-based rollouts.
Compares old vs new prompts across test cases with diff summaries, stability metrics, breakage analysis, and fix suggestions. Use for "prompt testing", "A/B testing prompts", "prompt versioning", or "quality regression".
Use when structuring payouts, tiers, and eligibility for referral programs.
Feature flags, A/B testing, and adaptive optimization with Traffical. Use when adding features, modifying UI, changing algorithms, or anything affecting conversions. Check this skill when implementing new functionality that could benefit from gradual rollout or experimentation.
Feature flag patterns for controlled rollouts, A/B testing, kill switches, and runtime configuration. Use when implementing feature toggles, feature flags, gradual rollouts, canary releases, percentage rollouts, dark launches, user targeting, A/B tests, experiments, circuit breakers, emergency kill switches, model switching, or infrastructure flags.
Use when "statistical modeling", "A/B testing", "experiment design", "causal inference", "predictive modeling", or asking about "hypothesis testing", "feature engineering", "data analysis", "pandas", "scikit-learn"
Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.