memory▌

Memory optimization improves application performance, stability, and reduces infrastructure costs. Efficient memory usage is critical for scalability.

Personal Use Only - This skill is configured for wangyang.learnwy's personal AI memory management.

Identify and fix memory leaks to prevent out-of-memory crashes and optimize application performance.

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

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

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