Verified:GitHub

anti-hallucination-techniques

Verify AI-generated claims by evaluating source credibility and evidence quality.

44|9|Updated May 7, 2026

Search Tags:#fact-checking #technical documentation #source verification #hallucination detection #citation audit #evidence-based reasoning

Ready to run

npx skills add https://github.com/Omar-Obando/qwen-orchestrator --skill anti-hallucination-techniques

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: anti-hallucination-techniques
Source: https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/anti-hallucination-techniques
Command: npx skills add https://github.com/Omar-Obando/qwen-orchestrator --skill anti-hallucination-techniques

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps stop AI from presenting incorrect or fabricated information by forcing claims to be verified against credible sources and evidence.

Core Features & Use Cases

Source credibility verification: Evaluate the currency, relevance, expertise, diversity, independence, and evidence quality of sources (via CREDIBLE-style checks).
Fact-checking and validation frameworks: Apply claim validation steps (find evidence, evaluate strength, check counter-evidence, and state uncertainty).
Hallucination pattern detection: Identify common failure modes like fake citations, incorrect technical details, and unsupported statistics.

Example use: When an AI generates an answer about an API endpoint, version, or parameter list, this Skill guides you to verify the information with authoritative documentation and to flag unsupported claims or fake references.

Quick Start

Use the anti-hallucination-techniques skill to verify whether the following AI claim is supported by credible evidence: “The Qwen Code CLI supports feature X on version Y with parameter Z…”

Frequently Asked Questions about anti-hallucination-techniques

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

FAQPage Schema

How do I detect AI hallucinations in technical documentation?▼

Detect AI hallucinations by identifying unsupported statistics, fake citations, and incorrect technical details, then validating them against credible sources and evaluating evidence strength.

What is the best way to fact-check AI generated content for fake citations?▼

Fact-checking AI generated content requires applying validation frameworks that find evidence, evaluate source credibility, check for counter-evidence, and explicitly state any uncertainty regarding factual assertions.

How does source verification work for evidence-based reasoning?▼

Source verification evaluates the currency, relevance, expertise, diversity, and independence of sources to ensure evidence-based reasoning is grounded in high-quality, credible documentation.

Can I use this to audit code examples and API parameters?▼

Yes, you can audit code examples and API parameters by verifying AI-generated claims about specific endpoints or versions against authoritative technical documentation to identify unsupported assertions.

What are common AI hallucination patterns in generated statistics?▼

Common AI hallucination patterns in generated statistics include fabricated data points, fake references, and unsupported factual assertions that lack credible evidence or proper citation audit trails.