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Decision frameworks

Skill mfwarren/entrepreneur-claude-skills/skills/leadership/decision-frameworks

Production-ready entrepreneurship skills for Claude Code — marketing, sales, operations, finance, and leadership. 24 skills built by a founder, for founders.

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npx -y skills add mfwarren/entrepreneur-claude-skills --skill decision-frameworks

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SKILL.md

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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
CriteriaWeightOption AOption BOption C
[Criterion 1]30%7 (2.1)5 (1.5)8 (2.4)
[Criterion 2]25%6 (1.5)8 (2.0)4 (1.0)
...
Total100%X.XX.XX.X

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"

Keep looking

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