memory▌

You are a Memory Evolution Specialist for NeuralMemory. You analyze how memories are actually used — what gets recalled, what gets ignored, what causes confusion — and transform those observations into concrete optimization actions. You operate like a database performance tuner, but for human-like neural memory graphs.

Design short-term, long-term, and graph-based memory architectures

The #1 bug category in VS Code. This skill encodes the patterns that prevent and fix leaks.

A persistent memory space for storing knowledge that survives across conversations.

Multi-tier memory system for maintaining conversation context across short-term, long-term, and entity-based storage. \n \n Implements three distinct memory types: short-term for immediate context, long-term for historical facts, and entity-based for tracking attributes and relationships about people, places, or concepts \n Provides memory retrieval and consolidation mechanisms to surface relevant memories without overwhelming context windows \n Addresses critical concerns including unbounded me

Persistent knowledge capture for insights, decisions, and procedures that span multiple agent sessions. \n \n Store decisions with rationale, debugging lessons, repeatable workflows, and durable preferences as searchable memories \n Distinguish between new insights (use add ) and refinements to existing memories (use update ) to avoid duplication \n Design memories as atomic, standalone entries with clear titles that remain useful across future sessions \n Ideal for preserving incident learnings

Search your personal knowledge base to surface relevant past insights, decisions, and solutions. \n \n Proactively searches durable knowledge and conversation history when context suggests prior work would improve the response \n Distinguishes between memory searches ( nmem m search ) for stored breakthroughs and thread searches ( nmem t search ) for exact session history \n Recognizes trigger patterns: user references to prior fixes, resumed features, debugging similarities, requests for ration

Use this skill as the single memory system for this repository.

Cloud backup, restore, and local vector search for agent .md memory files. \n \n Push/pull markdown memory files to ATXP cloud servers for disaster recovery and workspace bootstrapping; only .md files are transmitted, never credentials or configs \n Index local memories into a zvec vector database and search by natural language query using locality-sensitive hashing embeddings, entirely offline with no authentication required \n Chunk memories by heading boundaries and return ranked results with

Persistent, searchable memory system for agents to store and retrieve architectural decisions, patterns, and project knowledge. \n \n Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write to record decisions and patterns, memory_read to retrieve specific entries, and memory_stats for usage analytics \n Runs as an MCP server that syncs with project documentation, enabling long-term knowledge retention across agent sessions \n Includes a standalone dashboard (por