GRADE-on-Ingest streamlines the research workflow by ensuring every source added to your cognitive corpus is immediately evaluated for quality and reliability. By implementing an 'assess at entry' pattern, it extracts metadata, determines baseline quality levels—ranging from peer-reviewed journals to forum discussions—and delegates full assessments to specialized agents. This ensures that unverified or low-quality data is never cited without proper context or necessary hedging language, maintaining the integrity of your AI-augmented development projects.
主要功能
01Baseline quality mapping for diverse source types
02Integration with Citation Guard for evidence-based hedging
03146 GitHub stars
04Automatic quality assessment triggers for new research files
05Real-time corpus quality distribution reporting
06Automated metadata extraction and frontmatter updates
使用场景
01Automating peer-review assessments for technical documentation
02Maintaining a high-integrity research corpus for software development
03Enforcing evidence-based citation policies in AI-generated content