critical-thinking-genai

Tests model-generated text with eleven probes that verify claims against external sources.

13|5|Updated Jan 12, 2026
One-click install
npx skills add https://github.com/mlevison/agent-thinking-skills --skill critical-thinking-genai-mlevison
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: critical-thinking-genai
Source: https://github.com/mlevison/agent-thinking-skills/tree/main/skills/critical-thinking-genai
Command: npx skills add https://github.com/mlevison/agent-thinking-skills --skill critical-thinking-genai-mlevison

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? AI-generated text can look like sound reasoning while containing invented facts, fake citations, and silent omissions, and asking the model to explain itself only produces more confident text. This Skill tests the output itself instead of trying to walk an author back through reasoning that never happened. ## Core Features & Use Cases - Eleven Targeted Probes: Detect hallucinated citations, framing bias, false balance, vague sections, and silent omissions, selected based on what being wrong would cost. - Fresh Conversation Strategy: Recommends restarting with a short brief instead of correcting a contaminated thread, avoiding anchored errors. - External Verification Discipline: Directs every check to primary documents, real data, or people with direct knowledge rather than trusting a second model's agreement. - Use Case: You pasted a ChatGPT answer containing a statistic and a citation into a report. The Skill asks one probe at a time, identifies the claims that would collapse the answer if false, and tells you exactly where to verify each one. ## Quick Start Paste the AI-generated answer you are about to rely on and ask the assistant to verify this AI output before you use it.

Frequently Asked Questions about critical-thinking-genai

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

FAQPage Schema
How do I verify AI-generated output before using it?▼

Identify the claims that would collapse the answer if false, then check each against a primary document, actual data, or a person with direct knowledge. Open every citation, since sources that look right but do not exist are a routine model output.

How to detect hallucinations in ChatGPT or Claude answers?▼

Look where training data was thin: anything niche, local, recent, or specific to your situation like budget or jurisdiction. Ask for specifics at points where the answer turns vague, and confirm names, numbers, and versions against dated external sources.

Can I use a second AI model to fact-check the first one?▼

No, two models agreeing is not corroboration because both generate answers the same way. A second model can suggest where to look for evidence, but the actual verification must come from human-authored sources outside any model.

Should I ask the AI why it made a mistake?▼

No. The model produces a plausible, confident explanation unconnected to what actually happened, and the invented cause stays in the thread contaminating later replies. Restate the question in a fresh session with the missing constraint instead.

When should I start a fresh conversation instead of correcting the AI?▼

Start fresh whenever the thread has been carrying a wrong turn, since corrections revise the bad answer rather than replacing it. Carry a short brief with the restated question and new constraints, not the transcript or the earlier answer.

What are the limitations of AI self-verification?▼

A model has no reliable way to sort its own verified claims from invented ones and tends to defend text it produced. Self-checks catch errors only sometimes, with no way to tell which times, which is why every check must point outside the model.