曖昧、高値、複雑性傾向の問題で意思決定品質を向上させるための再利用可能なエージェントスキル。

このスキルは、LLMが実際の質問を明確にするのに役立ちます, 隠された仮定を公開します, 基本的な事実からの理由, 不要な複雑さを軽減します。, クリーナーの推奨または次のステップで終わる.

3つの補完的な推論モードを実用的なシーケンスで組み合わせます。

1.実質の問題を明確にするために疑問を抱える 2.第一主義思想 事実と制約の問題を減らす 3. 不必要な複雑さを取除くためにOccam様式の単純化

このスキルは?

このスキルは、エージェントを助けるように設計されています。

• 実際の決定や質問を特定する
• 隠れた仮定を公開する
• 慣性および慣習からの別の事実
• 制約、メカニズム、および因果構造に問題を減らす
• 複雑さと仮定負荷によるオプションを比較する
• 現実を削除せずに偽の複雑さを削除
• 推奨、次の移動、またはテストで終了

ベストユースケース

ユーザーが以下のようなことを尋ねるときに、このスキルを使用します。

• 私は欠落していますか?
• これは本当にこの方法でなければなりませんか?
• 私は削除する必要がありますか?
• まだ収まる最も簡単な説明は何ですか?

decision-clarity

A reusable agent skill for improving decision quality in ambiguous, high-value, complexity-prone problems.

This skill helps an LLM clarify the real question, expose hidden assumptions, reason from fundamental facts, reduce unnecessary complexity, and end with a cleaner recommendation or next step.

It combines three complementary reasoning modes in a practical sequence:

1. Socratic questioning to clarify the real issue
2. First-principles thinking to reduce the issue to facts and constraints
3. Occam-style simplification to remove unnecessary complexity

What this skill does

This skill is designed to help agents:

• identify the real decision or question
• expose hidden assumptions
• separate facts from inertia and convention
• reduce problems into constraints, mechanisms, and causal structure
• compare options by complexity and assumption load
• remove false complexity without deleting reality
• end with a recommendation, next move, or test

Best use cases

Use this skill when the user asks things like:

• What am I missing?
• Does this really have to be this way?
• What should I remove?
• What is the simplest explanation that still fits?
• Help me think this through.
• Which option actually makes the most sense?

It is especially useful for:

• startup and business decisions
• product and MVP decisions
• content and creator workflow design
• operations and process simplification
• strategy questions with multiple plausible options
• personal decisions distorted by confusion, self-justification, or excess complexity
• argument and reasoning audits

Structure

decision-clarity/
├── SKILL.md
├── Workflows/
│   ├── Clarify.md
│   ├── Deconstruct.md
│   ├── Simplify.md
│   └── Decide.md
└── references/
    ├── anti-patterns.md
    ├── business.md
    ├── content.md
    ├── examples.md
    ├── operations.md
    ├── output-patterns.md
    ├── product.md
    └── trigger-questions.md

Workflow model

1. Clarify

Identify the real question, clean up vague framing, and expose initial assumptions.

2. Deconstruct

Reduce the problem into facts, constraints, mechanisms, and causal structure.

3. Simplify

Remove unnecessary complexity and compare options by assumption load while preserving adequacy.

4. Decide

Convert the clarified, decomposed, and simplified reasoning into a practical recommendation, next move, or test.

Included references

• anti-patterns.md — failure modes such as endless inquiry, false simplicity, and analysis without action
• business.md — startup, pricing, growth, distribution, and strategy questions
• product.md — MVP, feature decisions, onboarding, and scope simplification
• content.md — tutorials, creator workflows, and audience-value decisions
• operations.md — SOPs, approvals, handoffs, and process simplification
• trigger-questions.md — transforms vague prompts into sharper analysis frames