research-wiki

A research knowledge base organizes papers, ideas, experiments, and claims into linked entities with traceable relationships, preventing fragmented research knowledge. It maintains a structured graph of connections, gaps, and outcomes throughout the entire research lifecycle.

Tracking literature and experiments across long-running projects requires maintaining canonical entities, graph edges, and lifecycle updates within a knowledge base. This enables continuous paper ingestion and experiment history logging without rebuilding context from scratch.

A knowledge graph supports hypothesis development and proof auditing by mapping research connections, evidence, and outcomes into structured graph data. It enables query summaries and validation checks to audit claims and guide future investigations accurately.

Yes, you can record failed ideas and track experiment history by ingesting papers and logging outcomes into a persistent knowledge graph. The system maintains validation checks and lifecycle updates for long-running research projects.

No specific dependencies or components are required to maintain a research knowledge graph. The skill operates independently to manage persistent research knowledge, organizing papers, ideas, and experiments into structured entities with automated graph updates.