contribute-knowledge-to-cogni

Automating knowledge contribution routes durable findings into a centralized hub using atomic, citable entries. This enforces provenance rules and edge-citation patterns, preventing context fragmentation across ephemeral sessions.

The best way to manage branches is enforcing a single-open-branch workflow. One principal holds an open contribution branch, and every write appends to it, preventing knowledge fragmentation and ensuring a coherent knowledge DAG.

Routing by content shape directs entries to specialized sub-skills based on their format. Falsifiable predictions route to edo-loop, human-readable visuals to dolt-human-visuals, and AI-readable text goes to direct knowledge storage.

Yes, you can recall and refine existing entries. The contribution workflow automatically recalls prior entries, cites parent nodes, and refines the content to avoid duplication and build a coherent knowledge DAG.

You need edge-citation patterns when adding atomic findings to the knowledge hub. Citing parent entries ensures provenance, linking new data points to existing knowledge nodes to maintain a structured and traceable knowledge graph.

Yes, the method supports visual data formats. Content shaped as human-readable visuals routes automatically to the dolt-human-visuals sub-skill, ensuring graphical findings are preserved with correct provenance alongside text entries.