Agent Skills
› MINT-SJTU/RoboClaw
› memory
memory
GitHub提供双层记忆系统,包含长期事实与事件日志。支持通过grep或脚本搜索历史对话,自动提取偏好与上下文至长期记忆,辅助Agent保持连续性与个性化服务。
Trigger Scenarios
需要回忆用户偏好或项目背景
搜索过往对话记录或事件日志
Install
npx skills add MINT-SJTU/RoboClaw --skill memory -g -y
SKILL.md
Frontmatter
{
"name": "memory",
"always": true,
"description": "Two-layer memory system with grep-based recall."
}
Memory
Structure
memory/MEMORY.md— Long-term facts (preferences, project context, relationships). Always loaded into your context.memory/HISTORY.md— Append-only event log. NOT loaded into context. Search it with grep-style tools or in-memory filters. Each entry starts with [YYYY-MM-DD HH:MM].
Search Past Events
Choose the search method based on file size:
- Small
memory/HISTORY.md: useread_file, then search in-memory - Large or long-lived
memory/HISTORY.md: use theexectool for targeted search
Examples:
- Linux/macOS:
grep -i "keyword" memory/HISTORY.md - Windows:
findstr /i "keyword" memory\HISTORY.md - Cross-platform Python:
python -c "from pathlib import Path; text = Path('memory/HISTORY.md').read_text(encoding='utf-8'); print('\n'.join([l for l in text.splitlines() if 'keyword' in l.lower()][-20:]))"
Prefer targeted command-line search for large history files.
When to Update MEMORY.md
Write important facts immediately using edit_file or write_file:
- User preferences ("I prefer dark mode")
- Project context ("The API uses OAuth2")
- Relationships ("Alice is the project lead")
Auto-consolidation
Old conversations are automatically summarized and appended to HISTORY.md when the session grows large. Long-term facts are extracted to MEMORY.md. You don't need to manage this.
Version History
- e9b28f9 Current 2026-07-25 05:39


