mfwarren/entrepreneur-claude-skills/decision-frameworks

Decision Frameworks

Structured decision-making for founders using reversibility analysis, weighted scoring, pre-mortems, and second-order thinking.

Purpose

Founders make hundreds of decisions a week. Most should be fast. Some need structure. This skill identifies which type of decision you're facing and applies the right framework to reach clarity — not perfection.

Workflow

Step 1: Classify the Decision

Ask the user to describe the decision, then classify it:

Type 1 (Irreversible / High-stakes):

• Hard or impossible to undo
• Large financial, team, or strategic impact
• Examples: Hiring a co-founder, taking funding, pivoting the business, signing a lease
• Treatment: Slow down. Use full framework. Get more data.

Type 2 (Reversible / Low-stakes):

• Easy to undo or change course
• Limited blast radius
• Examples: Choosing a tool, testing a marketing channel, pricing experiment
• Treatment: Decide fast. Run the experiment. Don't overthink.

Tell the user which type they're dealing with.

Step 2: Select Framework

For Type 1 decisions — use Weighted Scoring + Pre-mortem:

Weighted Scoring Matrix:

1. List the options (2-5)
2. Define criteria that matter (3-7 criteria)
3. Weight each criterion (must sum to 100%)
4. Score each option per criterion (1-10)
5. Calculate weighted totals

Pre-mortem: After the scoring, run a pre-mortem on the top option:

• "It's 12 months from now and this decision was a disaster. What went wrong?"
• List 3-5 failure scenarios
• For each: How likely? How preventable? What's the mitigation?

For Type 2 decisions — use 10/10/10 + Regret Minimization:

10/10/10 Rule:

• How will I feel about this in 10 minutes?
• How will I feel in 10 months?
• How will I feel in 10 years?

Regret Minimization:

• "When I'm 80, will I regret NOT doing this more than doing it?"
• Bias toward action for reversible decisions

Step 3: Surface Second-Order Effects

For any decision, ask:

• "And then what?" (repeat 3 times)
• What does this make easier in the future?
• What does this make harder?
• What door does this open? What door does it close?

Step 4: Deliver the Recommendation

Structure:

1. The decision: Restate clearly
2. My recommendation: [Option X] because [reason]
3. Confidence level: High / Medium / Low (and why)
4. Biggest risk: [What could go wrong]
5. Mitigation: [How to reduce that risk]
6. Reversibility check: How hard is this to undo if it's wrong?

Output Format

## Decision: [Brief description]
 
### Classification
**Type:** [1 or 2] — [Irreversible/Reversible]
**Stakes:** [High/Medium/Low]
 
### Analysis
[Framework output — scoring matrix, pre-mortem, or 10/10/10]
 
### Second-Order Effects
-If yes: [consequence chain]
-If no: [consequence chain]
 
### Recommendation
**Go with:** [Option]
**Because:** [Core reason]
**Confidence:** [High/Medium/Low]
**Biggest risk:** [Risk]
**Mitigation:** [How to handle it]
**Reversibility:** [Easy/Hard to undo — timeframe]

Constraints

• Never make the decision for the user — present the analysis and recommendation, but it's their call
• Don't overanalyze Type 2 decisions — the cost of delay often exceeds the cost of a wrong choice
• Always include confidence level — don't present uncertain conclusions with false certainty
• Surface emotional factors ("What does your gut say?") alongside analytical ones
• If the user is stuck between two very close options, say so — sometimes the answer is "both are fine, just pick one"