From Agent Memory to Portable Skills

Neo4j’s Agent Memory Service (NAMS) is presented as a graph-based system for converting agents’ accumulated conversations, organizational knowledge, reasoning traces, and tool-use histories into portable, executable SKILL.md packages. Its skill-distillation process scopes relevant memory, identifies recurring successful procedures, derives a structured execution graph from recorded traces, and uses an LLM only to synthesize source-cited descriptions and completion criteria, while deterministic checks validate provenance, coverage, coherence, schema compliance, and PII handling. Distilled skills remain linked through graph relationships to the evidence supporting each claim, require human review before publication, and can be consumed through Claude Code, MCP, REST, or other compatible agent frameworks. The system also monitors changes in underlying memory and tool outcomes for drift, enabling targeted repair of affected steps or requiring full re-distillation when evidence is no longer valid. NAMS adopts concepts from the Agent Instruction Protocol, which represents skills as typed, schema-validated graphs rather than prose instructions, an approach whose cited evaluation reported higher task pass rates and faster execution than freeform skill documentation.

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