AGI记忆模组

This skill addresses the lack of long-term memory and context management capabilities in AI agents. In practical development, agents often lose critical information during extended conversations and fail to effectively utilize historical interactions, leading to repetitive responses or context fragmentation. Through five core Context Engineering capabilities (Selection, Compression, Retrieval, State, and Memory), this skill builds a complete memory infrastructure for agents, ensuring each interaction accurately correlates with historical information.

Usage is straightforward: simply install the dependency in your project and configure the storage path. The skill will then run as a mandatory meta-skill. All interactions between users and the model automatically trigger memory processing without manual invocation. It operates through a four-layer architecture (Orchestration Layer, Cognitive Model Layer, Dual-Track Architecture, Storage Layer) working in concert to automatically perform noise filtering, relevance screening, chain structure extraction, result compression, conflict detection, and cross-session correlation.

It is particularly suitable for agent development teams needing to build long-term memory capabilities, especially for projects that have deployed AI assistants but face context loss issues. For scenarios requiring complex multi-turn dialogue handling, knowledge management, or task tracking, this skill significantly enhances agent coherence and accuracy. Backend developers and project managers can also benefit, as the skill provides observability interfaces and token budget management.

It is recommended to integrate this skill early in development to establish a solid memory foundation. Note that it depends on a Python environment and external services like Redis; ensure infrastructure is ready before deployment. As a meta-skill, it runs persistently and consumes a certain token budget, so configure budget limits appropriately to avoid unexpected costs.