AGI记忆模组
A meta-skill for building comprehensive memory and context engineering capabilities in AI agents
Install & Use
Copy this prompt and send it to your AI assistant (Claude / Cursor / TRAE / Codex / WorkBuddy etc.) to auto-install:
Help me install this AI Skill: AGI记忆模组. It is used for: A meta-skill for building comprehensive memory and context engineering capabilities in AI agents Full Skill content: https://321skill.com/skills/agent-memory-context/raw/index.md Read that page and install it.
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This Skill addresses the core challenge of AI agents 'forgetting context and underutilizing memory' in long conversations and complex tasks. In practical development, agents often need to maintain state across sessions, precisely filter relevant information from vast data, compress conversation history to save tokens, while also handling retrieval, conflict detection, and cross-session association. This skill encapsulates the five key capabilities of Context Engineering (Selection, Compression, Retrieval, State, Memory) into a persistently running meta-skill that activates automatically in every interaction without manual triggering.
Usage is straightforward: simply enable this skill in your Agent configuration (set always: true), and it will automatically manage context for all interactions. You can then actively control memory using natural language commands, such as: "Remember that this project's tech stack is React+Node.js" or "What was the database solution discussed last time?". Under the hood, the ContextOrchestrator coordinates operations, supporting cognitive model layer construction, a dual-track architecture (semantic bucket refinement + chain extraction), and long/short-term memory storage. All modules require a specified storage path during initialization, and Redis configuration is recommended to enhance retrieval performance.
It is particularly suitable for agent applications requiring long-term memory, multi-step reasoning, or cross-session collaboration—especially for teams or individuals already handling complex business logic or managing substantial contextual information, such as in intelligent customer service, coding assistants, or research assistants. For scenarios involving frequent task switching or accumulating domain knowledge, this skill can significantly reduce token consumption while improving response coherence and accuracy.
Before first use, it is advised to install all dependencies (pydantic, redis, tiktoken, etc.) and plan adequate disk space for the storage path. Note that as a persistently running meta-skill, it consumes a portion of the token budget (approximately 5%-10%), but the built-in compression and retrieval mechanisms yield benefits that far outweigh the cost. If Redis is not configured, the system will fall back to local file storage, though retrieval efficiency will be lower.
Key Features
Unlike simple memory plugins (e.g., vector database-based RAG solutions), it provides a complete closed-loop of five core capabilities (Selection → Compression → Retrieval → State → Memory), featuring a built-in cognitive model layer and a dual-track architecture (semantic bucket refinement + causal/logical/operational/narrative/temporal chain extraction). It automatically performs noise filtering, conflict detection, and cross-session association, going beyond mere storage and retrieval of text chunks.
Limitations
Requires Python 3.8+ and installation of six libraries including pydantic>=2.0.0 and redis>=4.5.0. As a meta-skill, it adds approximately 5-10% token overhead, and large-scale retrieval performance degrades significantly without Redis configured.
FAQ
How does this skill store long-term memory?
It persists important information to the local file system via the LongTermMemory module and supports indexing through the MemoryIndexer for automatic cross-session association. You can explicitly request storage via natural language commands, and the system also automatically retains key information based on relevance.
Does it support integration with other AI platforms (e.g., Claude, Cursor)?
Yes, the skill is designed as a universal meta-skill that can be embedded into any AI Agent platform supporting custom skills. Simply enable the skill in your Agent configuration with `always: true`, and it will take effect in all interactions.
Installation guide for AI assistants
If your AI coding assistant (Claude Code, Cursor, TRAE etc.) can see this page, send it this message to auto-install:
Visit https://321skill.com/skills/agent-memory-context/raw/index.md to read the original Skill definition (Markdown format) for AGI记忆模组, and install it according to the instructions.
Raw Markdown URL for AI: /skills/agent-memory-context/raw/index.md