Agent skill

thinking-occams-razor

Apply parsimony principle to prefer simpler explanations with fewer assumptions. Use for hypothesis selection in debugging, architecture decisions, and choosing between competing approaches.

majiayu000534★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill thinking-occams-razor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/analysis/thinking-occams-razor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Occam's Razor (Parsimony Principle) ## Overview Occam's Razor, attributed to 14th-century philosopher William of Ockham, states: "Entities should not be multiplied beyond necessity" (entia non sunt multiplicanda praeter necessitatem). When multiple explanations fit the evidence equally well, prefer the simplest one—the one with the fewest assumptions. **Core Principle:** Among competing hypotheses that explain the data equally well, select the one with the fewest assumptions. **Einstein's Corollary:** "Everything should be made as simple as possible, but no simpler." ## When to Use - Debugging: Multiple hypotheses could explain a bug - Architecture: Choosing between design approaches - Root cause analysis: Several causes seem plausible - Code review: Evaluating implementation complexity - Technical decisions: Selecting between tools or patterns - Incident response: Narrowing down failure causes Decision flow: ``` Multiple explanations exist? → yes → Do they explain the evidence equally? → yes → APPLY OCCAM'S RAZOR ↘ no → Prefer better explanation ↘ no → Use available explanation ``` ## The Process ### Step 1: Enumerate Competing Hypotheses List all plausible explanations for the

What's inside
Steps it walks through
  1. Overview
  2. When to Use
  3. The Process
  4. Step 1: Enumerate Competing Hypotheses
  5. Step 2: Count the Assumptions
  6. Step 3: Verify Explanatory Power
  7. Step 4: Test Simplest First
  8. Step 5: Escalate Complexity Only When Needed
  9. Complexity Assessment Framework
  10. Counting Complexity
  11. Example Comparison
  12. When Simplicity Yields to Complexity
  13. 1. Evidence Demands It
  14. 2. Domain Complexity Is Irreducible
Ships with 1 file
  • metadata.json
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About this skill
What does the thinking-occams-razor skill do?

Apply parsimony principle to prefer simpler explanations with fewer assumptions. Use for hypothesis selection in debugging, architecture decisions, and choosing between competing approaches.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill thinking-occams-razor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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