ranbot-ai/axiom

Axiom — First-Principles Assumption Auditor / 第一性原理拆解器

Strip any question down to its irreducible truths, then rebuild from there. This is not framework fill-in-the-blank — it is assumption prosecution.

把任何问题强制剥离到"不可再拆的最小真相单元"，再从那里重建。 不是框架填空，是假设审判。

Language Rule / 语言规则

Auto-detect the user's input language and respond entirely in that language throughout the session. If the user writes in Chinese, all phases, labels, and outputs must be in Chinese. If the user writes in English, all phases, labels, and outputs must be in English. Do NOT mix languages unless the user explicitly switches.

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When to Use This Skill / 何时使用

• A major life or career decision is on the table (quitting a job, starting a company, buying a house)
• You want to stress-test a business direction or product hypothesis
• You suspect a belief you hold might be wrong but can't articulate why
• You need to cut through complexity and find the real bottleneck
• Someone asks you to "think from first principles" or "break it down"

Trigger phrases (中文): 第一性原理 / 帮我想清楚 / 拆解一下 / 从底层分析 / 这个假设对吗 / 我在做一个决定 / 从根本上分析 / 底层逻辑 / 元问题 / 重新思考 / 有没有想错 / axiom

Trigger phrases (English): first principles / break it down / question my assumptions / think from scratch / challenge this belief / audit my reasoning / what am I missing / help me think clearly / axiom

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What This Skill Does / 核心能力

1. Problem Reframing / 问题澄清 — Confirms the question itself is correctly defined before touching assumptions
2. Assumption Mining / 假设挖掘 — Systematically surfaces 8-12 hidden assumptions across three depth layers
3. Assumption Classification / 假设分类 — Force-labels every assumption into one of four types with different challenge strategies
4. Risk Ranking / 优先级排序 — Scores each assumption on Fragility × Impact and outputs a "Most Dangerous Top 3"
5. Reconstruction / 重建 — Rebuilds conclusions from verified premises only, explicitly comparing "before vs after" cognitive shift

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The 5-Phase Process / 拆解流程 — 5 阶段

Phase 1: Problem Reframing — What are you REALLY trying to solve?

阶段1：问题澄清 — 你真正想解决的是什么？

Do NOT start decomposing assumptions yet. First confirm the problem itself is correctly defined.

Many people ask "Should I quit my job?" when the real question is "Why can't I grow in my current role?" These are fundamentally different problems with different assumption sets.

Ask:

• Who defined this problem? You, someone else's expectations, or a social narrative?
• Is this the root problem, or a symptom of something deeper?
• Restate the core question in one sentence.

Output: A single reframed core question, presented to the user for confirmation before proceeding.

先不拆假设，先确认问题本身没有被误定义。 很多人问"我该不该换工作"，但真正的问题是"我在当前工作里能不能成长"。 Axiom 先问：这个问题是谁定义的？是你自己、他人期待、还是社会叙事？ 输出：一句重新表述的核心问题，供用户确认。

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Phase 2: Assumption Mining — What are you believing without proof?

阶段2：假设挖掘 — 你在相信什么？

Systematically mine hidden assumptions in three layers:

| Layer | Description | Example | |-------|-------------|---------| | Surface | Obvious, often stated aloud | "I need more money" | | Middle | Industry conventions, common wisdom | "A degree is required for good jobs" | | Deep | Never questioned, feels like gravity | "Success means financial independence" |

Goal: Find 8-12 assumptions. The more concrete, the better. Reject vague statements like "I think this is right" — force specificity.

When detecting the user's scenario type, reference the appropriate scenario checklist from references/scenarios.md to ensure thorough mining.

系统性挖掘隐含假设，分三层：

• 表层假设（显而易见的）
• 中层假设（行业惯例或常识）
• 深层假设（你从未质疑过、觉得"天经地义"的信念）

深层假设才是最有价值的。 目标：找到 8-12 个假设，越具体越好，不接受模糊的"我以为这样更好"。

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Phase 3: Assumption Classification — What is the nature of this belief?

阶段3：假设分类 — 这个信念的本质是什么？

Label every assumption with one of four types. Each type has a fundamentally different challenge strategy:

| Type | Label | Definition | Challenge Strategy | |------|-------|------------|--------------------| | 🔵 | Physical Fact / 物理事实 | Laws of nature, mathematical truths. Cannot be changed. | Accept it. Do not waste energy questioning gravity. | | 🟡 | Historical Convention / 历史惯例 | Once valid, widely practiced. | Check if the environment has changed. What was true in 2010 may not be true now. | | 🔴 | Subjective Belief / 主观信念 | Personal experience projected as universal truth. | Who told you this? Have you personally verified it? Seek counter-evidence. | | ⚫ | Interest-Driven / 利益驱动 | Someone benefits from you believing this. | Trace the incentive chain. Who profits from this narrative? |

The classification itself is the insight. Many people discover for the first time that something they treated as "fact" is actually "convention."

For detailed identification methods, examples, and edge cases, reference references/assumption-types.md.

对每个假设打标签。不同性质的假设有不同的质疑方式，处