技能详情（站内镜像，无评论）
作者：Dennis @dennis-da-menace
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 16 · 16.1k · 330 current installs · 338 all-time installs
⭐ 16
安装量（当前） 338
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：agent-memory
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
This skill is internally coherent: it implements a local SQLite-backed agent memory (reads/writes ~/.agent-memory/memory.db by default), requires no credentials or external network access, and its files match the SKILL.md usage.
目的
Name, README, SKILL.md, CLI wrappers, examples, tests, and src/memory.py all describe and implement a local persistent memory system using SQLite. There are no unrelated requirements (no cloud credentials, no network libraries) that would contradict the stated purpose.
说明范围
SKILL.md instructs the agent to instantiate AgentMemory and call methods such as remember(), recall(), learn(), and track_entity(). The code implements exactly those behaviors and only accesses the configured SQLite DB path (default ~/.agent-memory/memory.db). There are no instructions that read unrelated system files, transmit data to external endpoints, or access unexpected environment variables.
安装机制
No install spec is provided (instruction-only skill). The package includes source files but does not declare external installs or downloads; requirements.txt is empty. This is low-risk from an install perspective.
证书
The skill declares no required environment variables, no credentials, and no config paths beyond an optional db_path constructor argument. This is proportional for a local memory store.
持久
The skill persists data to disk by default at ~/.agent-memory/memory.db and creates that directory if missing. always is false and the skill does not request elevated privileges, but it will store potentially sensitive text in an unencrypted SQLite file unless the integrator specifies a different db_path or provides encryption. Consider this permanant-on-disk persistence when deciding to install.
综合结论
This skill appears to do what it says: a local, file-backed memory for agents. Before installing, consider: 1) Data sensitivity — memories are stored in plaintext SQLite by default (~/.agent-memory/memory.db); avoid writing secrets (passwords, API keys) into it or point it to a custom path you control. 2) File permissions — restrict access to the DB file (e.g., chmod 600). 3) Backup / retention — decide how long you want memories kept or enabl…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Agent Memory」。简介：Persistent memory for AI agents to store facts, learn from actions, recall info…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/dennis-da-menace/agent-memory/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
# AgentMemory Skill
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
## Installation
```bash
clawdhub install agent-memory
```
## Usage
```python
from src.memory import AgentMemory
mem = AgentMemory()
# Remember facts
mem.remember("Important information", tags=["category"])
# Learn from experience
mem.learn(
action="What was done",
context="situation",
outcome="positive", # or "negative"
insight="What was learned"
)
# Recall memories
facts = mem.recall("search query")
lessons = mem.get_lessons(context="topic")
# Track entities
mem.track_entity("Name", "person", {"role": "engineer"})
```
## When to Use
- **Starting a session**: Load relevant context from memory
- **After conversations**: Store important facts
- **After failures**: Record lessons learned
- **Meeting new people/projects**: Track as entities
## Integration with Clawdbot
Add to your AGENTS.md or HEARTBEAT.md:
```markdown
## Memory Protocol
On session start:
1. Load recent lessons: `mem.get_lessons(limit=5)`
2. Check entity context for current task
3. Recall relevant facts
On session end:
1. Extract durable facts from conversation
2. Record any lessons learned
3. Update entity information
```
## Database Location
Default: `~/.agent-memory/memory.db`
Custom: `AgentMemory(db_path="/path/to/memory.db")`