What problem does it solve?
This Skill helps improve a draft or recent AI output when the first answer is not deep enough, misses risks, or needs stronger reasoning. It creates a structured refinement loop so users can push the model to reconsider and enhance content instead of accepting a shallow result.
Core Features & Use Cases
- Method-driven critique: Selects and applies elicitation methods such as Socratic questioning, first-principles analysis, red teaming, and pre-mortems based on context.
- Interactive refinement loop: Presents multiple critique options, lets the user apply one or more methods, and keeps iterating until the user chooses to proceed.
- Targeted content enhancement: Designed for improving a specific section, draft, or generated artifact without rewriting the entire document from scratch.
- Use Case: When a strategy memo, feature spec, or recommendation feels too generic, use this Skill to systematically pressure-test assumptions, surface blind spots, and produce a more rigorous final version.
Quick Start
Ask the AI to use bmad-advanced-elicitation on its last response to deepen the analysis with a red-team or first-principles critique.