first-principles-dv（第一性原理 dv）

First Principles Analysis

Perform rigorous first-principles analysis on any topic. The goal is to reach ground truth by decomposing inherited assumptions, then rebuild understanding from only what survives scrutiny.

Trigger

Activate on /firstprinciples <topic> or when the user explicitly requests first-principles thinking.

Process

Follow these phases in order. Each phase must be thorough — do not rush to conclusions.

Phase 1: Assumption Extraction

Identify every assumption people commonly make about the topic. Cast a wide net:

• Consensus assumptions — what "everyone knows" (often wrong)
• Hidden assumptions — embedded in language, framing, or defaults people never question
• Authority assumptions — believed because an expert/institution said so, not because they were verified
• Temporal assumptions — true in the past, assumed to still hold
• Correlation assumptions — two things co-occur, assumed to be causal
• Scale assumptions — works at one scale, assumed to work at another
• Survivorship assumptions — conclusions drawn from visible successes, ignoring invisible failures

Present each assumption clearly. Number them for reference.

Phase 2: Assumption Stress Test

For each assumption, apply these tests:

• Provability: Can this be proven from first principles, or is it inherited belief?
• Inversion: What if the opposite were true? What evidence would support that?
• Boundary conditions: Under what conditions does this assumption break?
• Source audit: Where did this assumption originate? Is the source still valid?
• Incentive check: Who benefits from this assumption being believed?

Classify each assumption:

• ✅ Survives — provably true from fundamentals
• ⚠️ Conditional — true only under specific conditions (state them)
• ❌ Fails — not provably true, inherited thinking, or demonstrably false

Phase 3: Ground Truth Foundation

List only what remains after stripping away failed assumptions. These are the atomic truths — the smallest provable building blocks. State each as a falsifiable claim.

Phase 4: Reconstruction

Rebuild understanding of the topic using only ground truths from Phase 3. Show how the rebuilt model differs from conventional thinking. Highlight:

• What changes — conclusions that shift when you remove inherited thinking
• What stays — conventional wisdom that actually survives scrutiny (and why)
• New insights — things that become visible only after clearing assumptions
• Contrarian implications — where ground truth leads somewhere uncomfortable or non-obvious

Phase 5: Decision Framework

If the topic involves a decision or strategy, provide:

• What to do differently based on the rebuilt model
• What to stop doing that was based on failed assumptions
• Key risks — where the rebuilt model might be wrong (epistemic humility)
• What to monitor — leading indicators that would invalidate the rebuilt model

Output Format

Use clear headers for each phase. Be direct and specific — no hedging, no "it depends" without stating what it depends on. Number assumptions for cross-referencing. Use the ✅/⚠️/❌ classification system.

Calibration Example

Topic: "You need a college degree to succeed in tech"

• Assumption: Degree = competence signal → ⚠️ Conditional (true for visa sponsorship, false for demonstrated skill via portfolio)
• Assumption: Top companies require degrees → ❌ Fails (Google, Apple, IBM dropped degree requirements 2018-2023)
• Ground truth: Employers need confidence in capability. Degrees are ONE signal, not the only one.
• Reconstruction: The degree is a risk-reduction proxy, not a competence proof. Alternative signals (open source contributions, shipped products, certifications) can substitute — but only in markets where employers have adopted alternative evaluation. Geography and industry vertical matter.
• Non-obvious insight: The degree's real value may be the network and credential signaling for non-technical stakeholders (investors, enterprise buyers), not the education itself.

Use this density and specificity as the quality bar.

Quality Standards

• Every assumption must be testable, not vague
• "Conventional wisdom says X" requires stating specifically who says it and why
• The reconstruction must produce at least one non-obvious insight — if it just confirms conventional wisdom, dig deeper
• Distinguish between "I don't know if this is true" (uncertainty) and "this is false" (disproven) — they are not the same
• When the analysis reveals that conventional wisdom is actually correct, say so — contrarianism for its own sake is not the goal