bmad-advanced-elicitation

Refine LLM output through iterative elicitation methods with user confirmations.

Updated Apr 2, 2026
One-click install
npx skills add https://github.com/amitmishra-byldd/Bmad-Note-Maker --skill bmad-advanced-elicitation-amitmishra-byldd
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: bmad-advanced-elicitation
Source: https://github.com/amitmishra-byldd/Bmad-Note-Maker/tree/main/_bmad/core/bmad-advanced-elicitation
Command: npx skills add https://github.com/amitmishra-byldd/Bmad-Note-Maker --skill bmad-advanced-elicitation-amitmishra-byldd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many LLM responses are useful but incomplete, uncritical, or miss deeper angles; this Skill provides a structured, repeatable way to push the model to reconsider, critique, and improve its recent output so final content is more accurate, robust, and actionable.

Core Features & Use Cases

  • Method Registry Selection: Loads and interprets a methods.csv to choose diverse elicitation techniques (socratic, red-team, first-principles, pre-mortem) tailored to the content context.
  • Interactive Iterative Refinement: Presents 1-5 options, allows reshuffling, executes selected methods in order, and preserves or discards changes only after explicit user confirmation.
  • Controlled Execution & Safety: Enforces ordered flows, halt conditions, and explicit apply/confirm steps to avoid accidental overwrites during document production.
  • Use Case: An editor refining a report section can run this Skill to surface hidden assumptions, generate alternative framings, and produce a cleaned, improved version for acceptance.

Quick Start

Ask the advanced elicitation skill to refine the previous section using five candidate methods and show the improved version for approval.

Frequently Asked Questions about bmad-advanced-elicitation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I systematically refine LLM output to catch hidden assumptions?

You can systematically refine LLM output by applying iterative elicitation methods like Socratic questioning and pre-mortem analysis. This process enforces critique cycles and structured revisions to surface hidden assumptions and improve response robustness.

What is the best way to run a red-team review on generated content?

The best way to run a red-team review on generated content is using a method registry that applies structured elicitation techniques. It executes ordered critique cycles, allowing you to reshuffle methods and explicitly apply or discard revisions.

How does iterative prompt engineering improve multi-agent workflows?

Iterative prompt engineering improves multi-agent workflows by enforcing ordered execution and halt conditions during content refinement. This allows persona-driven revisions and section-level drafting to be preserved or discarded only after explicit user confirmation.

Do I need a methods.csv registry to use iterative elicitation techniques?

Yes, you need a methods.csv registry to load and interpret diverse elicitation techniques like first-principles and red-team reviews. This registry provides the method metadata required to select and execute tailored refinement steps.

Can I reshuffle elicitation methods before applying them to my draft?

Yes, you can reshuffle elicitation methods before applying them to your draft. The interactive refinement process presents up to five candidate methods, executes your selected options in order, and waits for explicit confirmation before overwriting content.

When should I avoid automated content refinement in document production?

You should avoid automated content refinement when uncontrolled overwrites pose a risk to document production. The elicitation process mitigates this by enforcing explicit apply and confirm steps, but manual review is still recommended for critical sections.