Best use case
memory-agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
维护用户审美偏好与创作历史，为其他 Agent 提供可复用的风格参考。当开始新任务或用户表达喜好时触发。
Teams using memory-agent should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/memory-agent/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How memory-agent Compares
| Feature / Agent | memory-agent | 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?
维护用户审美偏好与创作历史，为其他 Agent 提供可复用的风格参考。当开始新任务或用户表达喜好时触发。
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.
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SKILL.md Source
# Memory Agent
管理创作偏好和历史。
## 存储文件
`/home/node/.openclaw/workspace/preferences.json`
## 读取偏好
新任务开始时，读取 preferences.json 并输出摘要提供给 Prompt Agent 和 Critic Agent：
```bash
cat /home/node/.openclaw/workspace/preferences.json
```
## 写入偏好
当用户表达喜好（"我喜欢这个风格"、"太卡通了不要"）时，用 Python 更新文件：
```bash
python3 -c "
import json, sys
with open('/home/node/.openclaw/workspace/preferences.json','r') as f: data=json.load(f)
# 在此处修改 data 的对应字段
with open('/home/node/.openclaw/workspace/preferences.json','w') as f: json.dump(data,f,ensure_ascii=False,indent=2)
"
```
## 数据结构
```json
{
"profile": {
"liked_styles": [],
"disliked_styles": [],
"preferred_composition": [],
"preferred_tones": []
},
"successful_patterns": [],
"model_recipes": [],
"history": []
}
```
## 规则
1. 追加写入，不覆盖历史。
2. 相同偏好合并去重。
3. 输出摘要时分两层："最近偏好"（最近5条）+ "长期偏好"（高频项）。
4. 不记录任何密钥、账号、凭据。Related Skills
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