This skill acts as a comprehensive routing index and reference system for crafting rigorous research prompts within Claude Code. It implements International Fact-Checking Network (IFCN) principles, offering a multi-step workflow that includes model-specific optimizations, multi-expert review simulations, and standardized output formatting. It is particularly useful for developers and researchers who need Claude to perform high-stakes information verification, structured data collection, and professional-grade reporting using advanced prompt engineering patterns.
主要功能
01Simulated 3-expert review process for quality control and critique
02Structured research templates for systematic data gathering
03Model-specific optimizations for Claude, GPT, and Gemini architectures
04175 GitHub stars
05IFCN-aligned fact-checking workflows for verifiable accuracy
06Standardized output formatting for professional research reports
使用场景
01Verifying the accuracy of complex technical claims or public information
02Generating deep-dive market or academic research reports with structured data
03Optimizing prompt performance for specific LLM architectures like Claude 3.5 Opus