Explorar skills

Analyze and recommend pricing structures and packaging options aligned with value delivery and business goals. Use this skill when a team needs to make or review pricing and packaging decisions.

Run a pre-mortem analysis to identify ways a product initiative could fail before it launches. Use this skill when a team wants to proactively surface failure modes and plan mitigations before committing to execution.

Evaluate whether a product experiment hypothesis is specific, measurable, and suitable for testing. Use this skill when the user already has a draft hypothesis and wants to know if it is strong enough for an A/B test or other product experiment.

Structure and interpret a cohort analysis to understand retention, engagement, or behavior patterns over time. Use this skill when a team needs to understand how different user groups behave across their lifecycle.

Analyze the outcome of a product experiment using control and test results, effect size, and statistical evidence. Use this skill when the user has experiment results and needs a clear readout of what happened.

Define the primary metric, secondary metrics, guardrails, and success criteria for a product experiment. Use this skill when the user has a hypothesis or planned test but has not yet defined how success should be measured.

Evaluate AI model capabilities, limitations, and trade-offs relevant to a product decision. Use this skill when a team needs to understand what an AI model can and cannot reliably do for their use case.

Map and analyze the competitive landscape to understand the competitive environment, key players, and strategic implications. Use this skill when a team needs a structured view of who they compete with and how the landscape is evolving.

Analyze the competitive landscape and define a clear positioning and differentiation angle for the product. Use this skill when a team needs to understand how they stand out and why customers should choose them.

Define the success metrics for an AI feature covering quality, user experience, business impact, and safety. Use this skill when an AI feature needs a complete metric framework before launch.

Design workflows that appropriately balance AI automation with human oversight and intervention. Use this skill when a team is building an AI feature and needs to decide when and how humans stay in the loop.

Define quality criteria for AI features and set up monitoring, evaluation, and iteration loops. Use this skill when a team needs to ensure their AI feature maintains acceptable quality in production.