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
Best use case
ai-runtime-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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
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
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "ai-runtime-memory" skill to help with this workflow task. Context: 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
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-runtime-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-runtime-memory Compares
| Feature / Agent | ai-runtime-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```bash
# 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
```bash
# 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](references/core/architecture.md)** - 分层记忆系统设计和实现原理
### 使用指南
- **[工具使用指南](references/guides/tools.md)** - memory_cli.py 和 memory_discovery.py 详细用法
### 高级主题
- **[维护指南](references/advanced/maintenance.md)** - 记忆固化、清理和质量保证
### 实践示例
- **[使用示例](references/examples/examples.md)** - 从基础查询到高级分析的完整示例
## 事件记录格式
### YAML Front Matter
```yaml
---
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
```python
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*Related Skills
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