moushi-decision

Compare competing options across trade-off dimensions and recommend principle-aligned choices.

Updated May 1, 2026

Search Tags:#decision analysis #opportunity cost #regret minimization #cognitive load reduction #option comparison #principle-based reasoning #memory writeback

Ready to run

npx skills add https://github.com/picsky/flowos --skill moushi-decision

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: moushi-decision
Source: https://github.com/picsky/flowos/tree/main/templates/skills/moushi-decision
Command: npx skills add https://github.com/picsky/flowos --skill moushi-decision

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Help users make clear decisions when they are stuck between options by turning ambiguity into structured trade-offs, constraints, and a user-owned final pick.

Core Features & Use Cases

Option trade-off analysis: Breaks down each choice by value alignment, opportunity cost, reversibility, second-order effects, time horizons, and regret minimization.
Principle-grounded recommendation: Retrieves relevant user principles from the principles library to ensure the analysis reflects the user’s long-term way of deciding rather than generic advice.
Decision logging for continuity: Writes the final decision (topic, options, user selection, and key reasons) into persistent state to support later review and possible principle extraction or planning updates.

Quick Start

Tell the assistant: “I’m纠结A vs B，该怎么选？请帮我分析利弊并给出建议，但最终我来拍板。”

Frequently Asked Questions about moushi-decision

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

FAQPage Schema

How do I resolve indecision between two competing options?▼

Resolving indecision involves breaking down each option across predefined dimensions like value alignment, opportunity cost, reversibility, and regret minimization to produce a structured trade-off comparison for a final recommendation.

How does principle-based reasoning work for decision analysis?▼

Principle-based reasoning retrieves your established user principles from a memory library to ground the option comparison, ensuring the final recommendation aligns with your long-term decision-making framework rather than generic advice.

What's the best way to compare opportunity cost and second-order effects?▼

Comparing opportunity cost and second-order effects requires an option-by-option evaluation across predefined dimensions, analyzing time horizons and potential regret to clarify the consequences of each choice before you make the final pick.

Can I log my final decision for future review and principle extraction?▼

Yes, the final decision, including the topic, options, your selection, and key reasons, is recorded via persistent memory writeback to support later review, continuity, and potential principle extraction.

Does decision analysis work for do-versus-don't choices?▼

Yes, decision analysis applies to A-versus-B choices, do-versus-don't decisions, and general requests for consequence analysis, turning ambiguity into structured trade-offs grounded in your personal principles.

How do I reduce cognitive load when comparing multiple options?▼

Reducing cognitive load involves structuring the comparison into predefined evaluation dimensions like reversibility and time horizons, which transforms ambiguous choices into a clear, manageable trade-off analysis.