think-question-burst

name	think-question-burst
description	Generates a rapid burst of questions about a problem (questions only, no answers), then ranks them for which would most change the approach and selects the single most catalytic one to pursue, producing a ranked question set. Use when you are stuck, too attached to one framing, or need a better question before answering.
license	Apache-2.0
metadata	{"id":"thinking-framework-skills.question-burst","family":"divergent-ideation","evidence-tier":"P","version":"0.1.0","standard":"0.8"}

Question Burst

Stuck thinking is usually stuck on the wrong question. A question burst generates many questions about a problem in a short, constrained burst (questions only, no answers), to break attachment to the current framing, then ranks them and picks the single most catalytic one. Because a model can generate questions endlessly, the value here is not the generation, it is the ranking and selection: this skill produces a ranked set ending in one chosen next question, never a bulk dump. The output is that ranked question set.

When to Use

• Stuck, or over-attached to a single framing of the problem.
• At the start of exploring an ambiguous problem, before committing to an answer.
• When a better question would unlock more than another answer.

When NOT to Use

• To produce a bulk list of questions with no ranking or selection (low signal; the main failure mode for an AI).
• When the issue needs answers and convergence, not more questions.
• When the catalytic question is already known.

Instructions

When asked to run a question burst, follow these steps:

1. State the problem in one line.
2. Burst. Generate roughly 12 to 20 questions about it. Questions only, no answers, no preamble. Mix angles: why, how, what-if, who, what-would-change-if. Keep it brief.
3. Rank. Order the questions by how much answering them would change the approach, not by how easy they are.
4. Select. Choose the single most catalytic "next question" and give a one-line reason it would shift the problem.
5. Emit the ranked question set per references/TEMPLATE.md.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the ranked questions plus the one chosen next question, not a flat list and not answers.

Quality Checklist

Before finalizing, verify:

• The burst was questions only, no answers.
• The questions are ranked by catalytic potential, not ease.
• Exactly one "next question" is selected with a reason.
• The output curates, it does not just dump a long list.
• The output is the ranked question set artifact.

Evidence

Tier P. The method is Hal Gregersen's question burst (MIT Sloan): generate many questions under a strict questions-only rule, then find the catalytic ones. MIT Sloan reports participant benefits (broader view, recognizing one's own role); there is no controlled decision-outcome evidence, and for AI the generation half has little value, so this skill is built around curation. Evidence is transferred from human workshops, not AI-validated. Full grading: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed ranked question set.