ReddRadar
Agent skill
ai-runtime-memory
AI Runtime分层记忆系统，支持SQL风格的事件查询、时间线管理，以及记忆的智能固化和检索，用于项目历史追踪和经验传承
Install this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dwsy/ai-runtime-memory
SKILL.md
AI Runtime 记忆系统
概述
AI Runtime的记忆系统采用分层架构，模拟人类大脑的记忆机制，实现持续存在和认知主体性。系统分为三个层次，通过专门的工具支持SQL风格查询和智能管理。
核心功能
三层记忆架构
- 短期记忆: 当前会话上下文，7±2组块限制
- 长期记忆: 跨项目技术知识，结构化知识图谱
- 情景记忆: 项目历史事件，支持复杂时间线查询
查询能力
- SQL风格条件查询（WHERE/ORDER BY/LIMIT）
- 多格式输出（table/json）
- 时间范围和标签过滤
- 全文搜索支持
快速开始
基本查询
# 进入记忆系统目录
cd .ai-runtime/memory
# 查看今天的事件
python3 memory_cli.py query --where "date='$(date +%Y-%m-%d)'"
# 查看架构决策
python3 memory_cli.py query --where "tags CONTAINS 'architecture' AND type='decision'"
使用便捷脚本
# 查看今天的事件
./scripts/memory-query.sh today
# 查看本周统计
./scripts/memory-query.sh week
# 搜索关键词
./scripts/memory-query.sh search "认证"
渐进式披露文档架构
基于 anthropics/skills 设计，按需加载详细信息：
核心架构
- 系统架构详解 - 分层记忆系统设计和实现原理
使用指南
- 工具使用指南 - memory_cli.py 和 memory_discovery.py 详细用法
高级主题
- 维护指南 - 记忆固化、清理和质量保证
实践示例
- 使用示例 - 从基础查询到高级分析的完整示例
事件记录格式
YAML Front Matter
---
id: unique-event-id
type: event|decision|error|meeting
level: day
timestamp: "2025-11-14T10:30:00"
tags: [architecture, decision]
---
目录结构
episodic/
└── 2025/11/14/
└── event-description.md
编程接口
from memory_discovery import MemoryDiscovery
# 初始化
discovery = MemoryDiscovery('.ai-runtime/memory')
# 查询
events = discovery.query(
where="date>='2025-11-14' AND tags CONTAINS 'architecture'",
order_by="timestamp desc",
limit=20
)
# 格式化输出
output = discovery.format_events(events, format_type="table")
相关命令
/runtime.remember- 记录新记忆事件/runtime.think- 基于记忆进行思考/runtime.explore- 探索和分析记忆模式
维护建议
- 定期运行
./scripts/memory-query.sh stats检查系统状态 - 每周审查
./scripts/memory-query.sh week的活动记录 - 每月归档重要事件到 long-term 记忆层
基于 anthropics/skills 渐进式披露架构设计
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