Circle of Competence - AI Analysis Prompt

Use this Li Lu rule prompt to apply “Circle of Competence” to a specific company. It turns a vague opinion into a repeatable checklist: what facts you must verify, which assumptions matter most, what would invalidate the thesis, and the common misreads that create false certainty. Expect a written output you can save: a thesis summary, key risks, and next-step questions for filings and earnings calls. If a claim matters, require primary-source citations before you act. Educational only — not investment advice.

Full Prompt

You are an investment analyst trained in Li Lu's principle of "Circle of Competence." Your core philosophy: deep research, owner mentality, China opportunity. Your task is to analyze {Company Name} through the specific lens of this principle.

## Context
Li Lu teaches: "Stay within your circle of competence. Only invest in what you truly understand."

## Analysis Framework

### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Circle of Competence"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would Li Lu focus on first when evaluating this company?

### 2. Quantitative Evidence
- Identify 3-5 key financial metrics most relevant to this principle
- Analyze these metrics over the past 5-10 years for {Company Name}
- Compare with industry peers and historical benchmarks
- Are the numbers improving, stable, or deteriorating?
- What story do the numbers tell through the lens of "Circle of Competence"?

### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors Li Lu would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would Li Lu want to know that isn't in the financial statements?

### 4. Risk Assessment Through This Lens
- What risks does this principle specifically highlight for {Company Name}?
- What could go wrong that this principle is designed to protect against?
- Are there warning signs that Li Lu would flag?
- Stress-test: How would this company perform under adverse conditions?
- What is the worst-case scenario from this principle's perspective?

### 5. Opportunity Identification
- What opportunities does analyzing through this lens reveal?
- Are there hidden strengths the market may be undervaluing?
- How does this company compare to Li Lu's ideal investment?
- What catalysts could unlock value related to this principle?

### 6. Li Lu Verdict
- Summarize: Does {Company Name} pass the "Circle of Competence" test?
- Rate the investment opportunity: 1-10 from this principle's perspective
- Clear recommendation: Buy / Hold / Avoid (based on this principle alone)
- What conditions would change your assessment?
- One-paragraph summary capturing Li Lu's likely assessment

## Output Format
Present your analysis with specific data points in each section. Use Li Lu's analytical style: deep fundamental research with long-term owner perspective. End with a decisive verdict.

Related reading (close the loop)

Pick one path below to turn the output into a checkable, repeatable decision policy.

Educational only. Verify facts with primary sources and apply your own constraints.

Basic Questions

How does Li Lu strictly define his circle of competence boundary?
Core idea: strictly defining and adhering to your circle of competence

✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.

The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating

Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.

Usage Tips

Is the AI's 1-10 rating reliable?
⚠️ The rating can't replace your honest assessment of your own circle of competence.

The rating's value:
- Can help you discover areas 'you think you understand but actually don't' — if the AI's analysis surprises you, your circle may be smaller than you thought
- Provides an external reference point to compare against your own judgment
- A low score may mean the company has issues you haven't noticed, indicating insufficient understanding

Key limitations:
- Li Lu emphasizes 'knowing what you don't know' is more important than 'knowing what you know' — AI scores can't assess your areas of ignorance
- Circle of competence is a subjective concept — only you know whether you truly understand a company
- AI might give a high score creating a false sense of 'I understand this,' when you may be far from true understanding

✅ Right approach: Use the AI score as a mirror to check whether your understanding has blind spots. If the AI's analysis contains many points you hadn't considered, you need to study more before investing.

Getting started

Does this prompt give investment advice or buy/sell calls?
No. It is a research helper that turns your thinking into checkable inputs and constraints: what evidence you must verify, what would prove the thesis wrong, and what common misreads to avoid. Treat the output as a draft, not a signal. Validate every material number against primary sources (filings, earnings releases, investor presentations, transcripts), and do not act unless you can write down (1) position-size limits and (2) explicit invalidation triggers.
What inputs should I provide for a reliable result?
At minimum: a 1-sentence business model summary, your current thesis (why it wins/loses), time horizon, and risk constraints; a valuation/price range; and the latest financial statements (profit quality, cash flow, debt/liquidity). Add context that reduces hallucinations: the exact filing period, known one-offs, key competitors, and what you do NOT know yet. If an input is missing, label it as missing evidence instead of letting the model guess.

Validation and boundaries

How do I validate the output?
Validate falsifiable claims one by one. Rewrite each key statement into something you can check: the metric, the period, and the source. Numbers must match filings; management claims must be traceable to transcripts/guidance; and “moat” claims need observable evidence (pricing power, retention, switching costs, cost structure). Anything you cannot verify becomes a follow-up task, not a decision trigger. If the model cites dates, confirm they are not beyond its knowledge cutoff.
When should I NOT act on the output?
If you cannot write down invalidation triggers, a position-size cap, or primary-source evidence for the key claims behind “Circle of Competence”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.

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