What is the 'is-ought' problem in reasoning?

The is-ought problem, or Hume's Guillotine, is the logical principle that you cannot derive a prescriptive statement (what ought to be) solely from a descriptive statement (what is) without an intervening value judgment or 'bridge' logic.

Can Hume's Guillotine be applied to business strategy?

Yes, it is highly effective for evaluating data-driven recommendations, forcing stakeholders to explicitly state and defend the strategic priorities that connect market facts to suggested actions.

Does using this skill slow down the decision-making process?

While it requires more rigorous thought, it ultimately speeds up consensus by identifying the true source of disagreement—whether it's about the facts (the 'is') or the underlying values (the 'ought').

How does this Claude Code skill help with AI development?

It helps developers and researchers identify when they are embedding subjective values into algorithms under the guise of objective data analysis, ensuring more transparent and ethical AI design.

Hume's Guillotine Reasoning

Name: Hume's Guillotine Reasoning
Author: lev-os

bylev-os

0•

Productivity & Workflow

Analyzes the gap between factual observations and prescriptive actions to ensure logical and ethical decision-making.

Hume's Guillotine is a specialized thinking pattern for Claude that helps users distinguish between descriptive statements (facts) and prescriptive statements (values). By identifying the 'is-ought' problem, this skill forces the explicit statement of hidden normative assumptions, enabling more rigorous ethical analysis, clearer policy development, and better-justified strategic decisions. It is particularly useful for AI ethics, technical architecture debates, and product strategy where data is often used to justify specific actions without making underlying values explicit.

Key Features

01Provides a structured framework for bridging facts and action plans

02Detects rhetorical sleight-of-hand in business and policy debates

03Identifies logical leaps from empirical data to normative recommendations

04Surfaces hidden value judgments and normative premises in arguments

05Analyzes ethical trade-offs in AI system design and algorithmic fairness

060 GitHub stars

Use Cases

01Scrutinizing technical policy arguments in documentation and research findings

02Evaluating AI ethics and bias mitigation strategies in software design

03Analyzing business strategy recommendations derived from market research data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lev-os/agents humes-guillotine

For use in Claude.ai and ChatGPT

Download Skill