thinking-occams-razor

Identify the simplest explanation fitting the evidence from competing hypotheses.

941|129|Updated Jan 28, 2026
One-click install
npx skills add https://github.com/tjboudreaux/cc-thinking-skills --skill thinking-occams-razor
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: thinking-occams-razor
Source: https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-occams-razor
Command: npx skills add https://github.com/tjboudreaux/cc-thinking-skills --skill thinking-occams-razor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Parsimony-guided decision-making helps teams select clear explanations with fewer assumptions, speeding debugging, design choices, and risk assessment.

Core Features & Use Cases

  • Systematic evaluation: Enumerate competing hypotheses and list required assumptions.
  • Powerful prioritization: Compare explanatory power and simplicity to pick the best explanation.
  • Practical guidance: Apply the simplest explanation first, escalating only with evidence.
  • Use Case: When investigating a bug with multiple potential causes, apply Occam's Razor to identify the most likely root cause and reduce investigation time.

Quick Start

Apply Occam's Razor to state the simplest explanation that fits the evidence and test it first.

Frequently Asked Questions about thinking-occams-razor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is Occam's Razor and how does it apply to root-cause analysis?

Occam's Razor is a problem-solving principle stating the simplest explanation fitting the evidence is most likely correct. In root-cause analysis, it systematically identifies likely causes by comparing explanatory power and testing the simplest option first.

How do I identify the root cause of a bug when multiple hypotheses exist?

To identify a bug's root cause, enumerate competing hypotheses and list required assumptions for each. Compare explanatory power and simplicity, then test the hypothesis with the fewest assumptions first to reduce investigation time.

Can I use parsimony-guided decision-making for software architecture choices?

Yes, parsimony-guided decision-making applies to software architecture choices by systematically evaluating competing design hypotheses, comparing their required assumptions, and prioritizing the simplest explanation with the highest explanatory power.

What's the best way to evaluate competing debugging hypotheses?

The best way to evaluate competing debugging hypotheses is to list supporting assumptions for each, compare their explanatory power and simplicity, and apply the simplest explanation first, escalating to complex options only with new evidence.

When should I escalate beyond the simplest explanation during debugging?

You should escalate beyond the simplest explanation during debugging only when new evidence invalidates the initial hypothesis. Apply the simplest explanation first and escalate to more complex root-cause hypotheses solely when evidence demands it.