hierarchical-memory-index

A multi-tier memory system improves information retrieval by organizing data into Immediate, Short-Term, and Long-Term tiers. This structure reduces context bloat by promoting frequently accessed facts to higher tiers while demoting less relevant data.

You manage fact retention through an indexing loop that evaluates reference frequency and size thresholds. Facts are audited, synthesized, indexed, and verified to ensure your knowledge base maintains a balanced memory system with optimal recall.

The best way to promote a fact to long-term memory storage is by running a targeted search with a domain tag. The indexing loop verifies the result and migrates the fact to the Long-Term Crystallized Core for permanent access.

Yes, you can use hierarchical memory indexing to maintain a structured knowledge base. It applies robust indexing and verification mechanisms to ensure efficient information storage and quick access for large corpus analysis.

Context bloat occurs when a memory system lacks multi-tier management and demotion mechanisms. Without indexing facts based on reference frequency and size thresholds, inactive data consumes retrieval context and degrades recall efficiency.

No specific dependencies are required to implement this multi-tier memory system. The skill operates independently using internal scripts and references to execute the audit, synthesis, indexing, and verification workflow.