Verify factual claims in articles using OpenAI GPT models and web search.
npx skills add https://github.com/MigoXLab/dingo --skill dingo-verify-migoxlab
Please help me install this Agent Skill. Skill: dingo-verify Source: https://github.com/MigoXLab/dingo/tree/main/.claude/skills/dingo-verify Command: npx skills add https://github.com/MigoXLab/dingo --skill dingo-verify-migoxlab
This Skill enables users to fact-check articles and verify factual claims in documents with high accuracy and ease.
Run the fact-check script with the article path: python ${CLAUDE_SKILL_DIR}/scripts/fact_check.py article_path.md
High-intent search queries and answers about installing and using this skill.
To fact-check articles, use the Dingo ArticleFactChecker script to validate factual claims and generate a detailed accuracy report. It utilizes OpenAI's GPT models and optional Tavily API for web search evidence verification.
Article fact-checking supports Markdown, plain text, JSONL, and JSON file formats. You can pass any of these file types directly to the fact-check script for automated claim validation.
Validate data quality in machine learning datasets by running the fact-check script against your training data files. It assesses factual accuracy in LLM training data using Dingo's ArticleFactChecker and GPT models.
Yes, article fact-checking requires Python and environment variables for API keys. You must configure OpenAI API keys for GPT model access, and optionally add Tavily API keys for web search verification.
Yes, you can select from a range of LLM models for fact-checking to balance accuracy and speed requirements. The ArticleFactChecker allows custom model selection to suit different verification needs.
The best way to verify factual claims in plain text is running `python fact_check.py article_path.md`. Dingo's ArticleFactChecker cross-references claims with web search evidence to produce a detailed verification report.
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