explore-decision-space

Expands problem frames and solution options before consequential decisions converge prematurely.

Updated May 12, 2026
One-click install
npx skills add https://github.com/mtk177a/skills --skill explore-decision-space-mtk177a
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: explore-decision-space
Source: https://github.com/mtk177a/skills/tree/main/skills/explore-decision-space
Command: npx skills add https://github.com/mtk177a/skills --skill explore-decision-space-mtk177a

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Consequential, hard-to-reverse decisions often collapse onto the first problem frame or solution anchor before alternatives are considered. This Skill widens the unsettled layer of a decision—problem space, solution space, or both—so choices are narrowed only with explicit evidence and constraints. ## Core Features & Use Cases - Problem-frame expansion: Surfaces materially different problem frames with the evidence that would distinguish them when the problem itself is unsettled. - Solution-space expansion: Generates structurally different solution options along explicit diversity axes once the problem frame is fixed. - Evidence-bounded narrowing: Separates observed facts, assumptions, and unknowns, and refuses to declare a winner without supporting evidence or authorized user preference. - Use Case: A team plans to rebuild onboarding because completion is low and has anchored on shortening the form. The Skill produces distinct problem frames (access issues, terminology confusion, form length) and the observations needed to distinguish them before any fix is chosen. ## Quick Start Ask the agent to expand the decision space and surface materially different problem frames or solution options before committing to the current approach.

Frequently Asked Questions about explore-decision-space

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

FAQPage Schema
How do I avoid premature convergence on one solution in AI-assisted planning?▼

Use decision-space exploration to generate structurally different options along explicit diversity axes such as mechanism, responsibility boundary, and state representation. Selection happens only when evidence, constraints, or an authorized user preference supports it.

What is the difference between problem-space and solution-space exploration?▼

Problem-space exploration questions what problem, causal structure, or success definition should govern the decision. Solution-space exploration assumes the problem frame is fixed and varies the mechanism or design structure of candidate solutions.

When should I use decision-space exploration versus clarifying a request?▼

Use clarification when purpose, success criteria, constraints, or authority are missing. Use decision-space exploration when the request is clear but the decision is anchored on one frame or option and needs materially different alternatives.

Can this Skill implement the option it recommends?▼

No. The workflow is strictly read-only: it does not edit files, run experiments, change evaluation assets, or implement options. It returns a completion state and hands off to design or implementation workflows after selection.

Does decision-space exploration require multiple agents or a fixed number of options?▼

No. The Skill does not force a fixed number of frames, options, iterations, or agents, and it does not require subagents or multi-agent workflows. Automated evolutionary search and score-driven mutation are explicitly out of scope.