Circle Of Competence Check

Circle of Competence Check

Determine whether a decision or opportunity falls within, outside, or in the "too hard" pile of your genuine expertise.

When to Use

• Evaluating whether to pursue an opportunity
• Deciding whether to give advice or an opinion on a topic
• Assessing whether you're qualified to make a particular decision
• Recognizing when to say "I don't know" or "this isn't for me"
• User asks "Is this within my competence?" or "Should I pursue this?" or "In, Out, or Too Hard?"

Inputs

Munger's Circle of Competence Principle

"Knowing what you don't know is more useful than being brilliant."

"We have three boxes: 'In,' 'Out,' and 'Too hard.' You don't have to be a jack-of-all-trades. At the Olympics, if you ace the 100 meters, you're not expected to throw the shotput."

"Each of you will have to figure out where your talent lies. And you'll have to use your advantages. But if you try to succeed in what you're worst at, you're going to have a very lousy career."

The size of your circle doesn't matter nearly as much as knowing exactly where the edge is. Operating within your circle means you can evaluate claims, detect errors, and predict outcomes. Outside it, you're guessing—and you don't know you're guessing.

The Three Boxes

IN (Within Circle of Competence)

You can:

• Evaluate claims and detect errors
• Predict likely outcomes based on patterns you've seen
• Identify what others are missing
• Know when something is unusual
• Ask the right questions
• Recognize quality vs. mediocrity

Requirement: You've done this before, studied it deeply, or have genuine expertise. Not "I read an article once."

OUT (Outside Circle of Competence)

You cannot:

• Verify claims made by others
• Distinguish good from bad advice
• Predict outcomes with confidence
• Recognize warning signs
• Know what you don't know

Key indicator: You'd have to trust others' judgment because you can't evaluate for yourself.

TOO HARD (Even for Experts)

Even specialists struggle because:

• The domain is inherently unpredictable
• Too many variables interact chaotically
• Track records are poor even among experts
• Success requires luck, not just skill

Key indicator: Nobody has a strong track record. Examples: Short-term market timing, predicting political outcomes, forecasting technology adoption rates.

The Framework (7 Steps)

Step 1: Define the Domain What specific area of expertise does this decision require? Be precise.

Step 2: Assess Your Experience What is your actual experience in this domain? Not "interest" but hands-on experience, formal study, or track record.

Step 3: The Verification Test Could you verify or falsify claims made by others in this domain? If someone told you something, could you evaluate whether it's true?

Step 4: The Warning Sign Test Would you recognize red flags that insiders would catch? Do you know what "bad" looks like here?

Step 5: The Prediction Test Have you made predictions in this domain that turned out correct? Do you have a track record?

Step 6: Classify the Opportunity Based on Steps 2-5, is this In, Out, or Too Hard?

Step 7: Decide on Action

• In: Proceed with analysis
• Out: Pass, or invest time to bring inside circle before deciding
• Too Hard: Pass without regret—even experts can't reliably win here

Workflow

Step 1: Gather and Review Inputs

Collect all relevant information:

• Review the provided data and context
• Identify key parameters and constraints
• Clarify any ambiguities or missing information
• Establish success criteria

Step 2: Analyze the Situation

Perform systematic analysis:

• Identify patterns and relationships
• Evaluate against established frameworks
• Consider multiple perspectives
• Document key findings

Step 3: Generate Recommendations

Create actionable outputs:

• Synthesize insights from analysis
• Prioritize recommendations by impact
• Ensure recommendations are specific and measurable
• Consider implementation feasibility

Output Format

## Circle of Competence Check

### Opportunity/Decision
[What you're evaluating]

### Required Expertise Domain
[The specific knowledge/experience this requires]

### Your Background Assessment

**Relevant experience:**
- [Experience 1]
- [Experience 2]
- ...

**Formal study/training:**
- [Education/training relevant to domain]

**Track record:**
- [Past decisions in this domain and outcomes]

### Competence Tests

| Test | Result | Evidence |
|------|--------|----------|
| Verification Test | Pass/Fail | [Can you evaluate claims?] |
| Warning Sign Test | Pass/Fail | [Would you spot red flags?] |
| Prediction Test | Pass/Fail | [Have you predicted correctly?] |

### Classification: [IN / OUT / TOO HARD]

### Reasoning
[Why this classification]

### Recommended Action
[What to do given this classification]

### The Munger Test
"Knowing what you don't know is more useful than being brilliant."
[Key insight about your competence boundaries]

Circle Expansion vs. Passing

When something is OUT, you have two options:

Option A: Pass

• Accept this isn't your domain
• No shame in missing opportunities outside your circle
• Protects you from losses
• Frees time for opportunities in your circle

Option B: Invest to Expand Circle

• Requires sustained study over months/years
• Not reading an article, but genuine expertise development
• Only worth it if:
  • The domain is important enough
  • You have genuine interest
  • You can commit the time

Warning: Most people overestimate how quickly they can expand their circle. Superficial knowledge creates the illusion of competence while leaving you vulnerable.

Constraints

• Brutal honesty required - The exercise is useless if you're not honest about limitations
• Interest ≠ competence - Being fascinated by something doesn't mean you understand it
• Past success may be luck - One good outcome doesn't prove competence
• Specificity matters - "Business" is too broad. "SaaS unit economics" is specific.

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Example

Input: "A friend wants me to invest in his DeFi project. I've made money in crypto before and I understand blockchain at a high level."

Output:

Why this works:

This example demonstrates the key principles of the skill in action. The approach is effective because:

• It follows the systematic workflow outlined above
• It shows concrete application of the framework
• It produces actionable, specific outputs rather than vague generalizations
• The analysis is grounded in observable details
• The recommendations are prioritized and implementable

Alternative applications:

This same approach can be applied to:

• Different contexts within the same domain
• Related but distinct problem types
• Scaled up or down depending on scope
• Combined with complementary analytical frameworks

Circle of Competence Check

Opportunity/Decision

Invest in friend's DeFi (Decentralized Finance) project.

Required Expertise Domain

This decision requires expertise in:

• DeFi protocols and smart contract mechanics
• Token economics and incentive design
• Smart contract security and audit evaluation
• Regulatory risk assessment
• Team technical capability evaluation
• Competitive landscape in DeFi

Your Background Assessment

Relevant experience:

• Made money in crypto before (unspecified mechanism)
• "Understand blockchain at a high level"

Formal study/training:

• None mentioned

Track record:

• Made money in crypto (but: market timing or skill? Bull market or contrarian insight?)

Competence Tests

Classification: OUT

Reasoning

You have surface familiarity, not operating competence.

Critical gaps:

1. "High level blockchain understanding" ≠ evaluating smart contract security
2. Making money in crypto doesn't demonstrate DeFi expertise
3. Friendship adds bias (Liking Tendency) that compounds the competence problem
4. You cannot independently verify any claims your friend makes about the technology

This is precisely the situation Munger warns about: you feel competent because you're interested and have adjacent experience, but you couldn't distinguish a legitimate project from a sophisticated scam.

The friendship factor makes this worse. Liking Tendency will cause you to:

• Interpret ambiguous information favorably
• Avoid asking hard questions
• Rationalize red flags you do notice

Recommended Action

Pass on this investment.

It's not in your circle, and friendship bias compounds the risk. If the project is good, you'll miss an opportunity—but you've missed countless opportunities outside your circle, and you're fine. If the project fails (which is the base rate for early-stage crypto projects), you preserve capital and friendship.

If you want to participate anyway:

1. Invest only an amount you can afford to lose completely
2. Do NOT tell yourself it's "within your competence" after making the investment
3. Have an independent expert (not chosen by your friend) evaluate the project
4. Recognize that you're speculating, not investing

The Munger Test

"Knowing what you don't know is more useful than being brilliant."

You know you're interested in crypto. You don't know whether this specific DeFi project is sound. That gap is where losses live. Your friend's enthusiasm cannot close it; only genuine expertise can.

"We have three boxes: In, Out, and Too Hard. This goes in Out."

Integration

This skill is part of the Charlie Munger expert persona. Use it to know precisely where your circle of competence ends—because that's where the danger begins.