From memory to behavior: skills, user profiles, and the consolidation loop

Neo4j’s meta-knowledge-graph approach addresses the limits of storing agent memories as isolated fragments by consolidating approved learnings into reusable task skills and user-specific profiles. Task learnings are safety- and consistency-checked, grouped through shared task patterns, and periodically converted into procedures containing use cases, steps, pitfalls, and verification methods, while preserving links to their originating sessions and error records for traceability and later revision. Skills are discovered incrementally through MCP search and fetch tools or prompt-time suggestions, reducing context use by loading full instructions only when needed, and their source fragments leave ordinary recall once consolidation is complete. Separately, approved user-scoped facts are summarized into compact, versioned profiles that capture cross-project preferences without altering the shared system prompt, allowing each user to receive personalized adaptations at session start. Hook-triggered background services, thresholds, cooldowns, optional human review, and stale-data handling govern both processes, aiming to let agents turn accumulated experience into maintainable guidance while retaining provenance and the ability to correct outdated knowledge.

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