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在 OpenClaw 中安装
/install rookie-memory
功能描述
Rookie-Memory 三级记忆管理系统 v2.0。专为 AI 代理设计的进化版记忆系统，包含 L0 永久记忆、L1 短期记忆、L2 中期记忆，支持 bootstrap 启动加载、autosave 自动保存、混合检索、自动清理等高级功能。
安全使用建议
This skill mostly does what it says (manages short/medium/long memories and uses embeddings + ChromaDB), but it also looks for OpenClaw platform credentials and will call an external embedding API by default. Before installing or running it: (1) inspect scripts/memory_manager.py yourself and confirm you're comfortable with it reading /root/.openclaw/openclaw.json (or run it in an isolated container or sandbox); (2) if you don't want the skill to use external providers, set ZHIYI_API_KEY to an empty value and/or change the embedding call to a local/no-op; (3) ensure the workspace path and virtualenv references are appropriate for your environment (the code uses hardcoded /root/.openclaw paths); (4) audit the OpenClaw config file for secrets and restrict permissions if necessary. If you cannot verify these things, treat the skill as potentially risky and avoid running autosave/cleanup operations that write/read platform config or send data externally.
功能分析
Type: OpenClaw Skill
Name: rookie-memory
Version: 2.0.0
The 'rookie-memory' skill is a comprehensive three-tier memory management system (Short-term, Medium-term, and Long-term) for OpenClaw agents. It utilizes local JSON files for sliding windows and summaries, and ChromaDB for vector-based long-term storage. The core logic in `scripts/memory_manager.py` includes legitimate features like automatic summarization, health analysis, and memory cleanup. While the script accesses `/root/.openclaw/openclaw.json` to automatically retrieve API keys for the Zhipu AI (BigModel) embedding service, this behavior is functionally aligned with its stated purpose of providing vector search capabilities and does not involve data exfiltration or unauthorized execution.
能力评估
Purpose & Capability
The skill's stated purpose (short/medium/long-term memory management) justifies use of embeddings and a local vector DB (ChromaDB). However, the code attempts to read /root/.openclaw/openclaw.json to extract a ZHIYI API key and defaults to calling an external embedding service (open.bigmodel.cn). The registry metadata declares no required environment variables or config paths, so accessing the platform config and external model provider is disproportionate to what was declared.
Instruction Scope
SKILL.md instructs running the included script and mentions using a venv with chromadb, and file IO under a workspace memory folder. The runtime code goes further: it will try to read OpenClaw's config file to extract API keys and will POST text to an external embedding API. Those actions (reading platform config, using discovered credentials, and outbound network calls) are not clearly documented in SKILL.md and expand the agent's scope beyond local memory management.
Install Mechanism
There is no install spec (instruction-only + included script), so nothing is automatically downloaded or installed. The Python script depends on requests and chromadb; SKILL.md examples reference a specific virtualenv path. Lack of declared dependencies is a quality/operational issue but not an installation red flag by itself.
Credentials
Registry metadata lists no required environment variables or config paths, yet the code reads environment variables (ZHIYI_BASE_URL, ZHIYI_API_KEY) and, if not set, opens /root/.openclaw/openclaw.json to find API keys. This accesses potentially sensitive credentials belonging to the platform or other skills without disclosure. The script will use any discovered API key to call an external embedding service, which could lead to credential use/exfiltration if unexpected.
Persistence & Privilege
The skill does not request always: true and is user-invocable. It writes memory files under a workspace (default /root/.openclaw/workspace/memory) and creates collections in a local ChromaDB path. That per-skill storage is expected. The concern is that it also reads a platform-level config file (/root/.openclaw/openclaw.json), touching configuration outside its own storage scope.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install rookie-memory - 安装完成后，直接呼叫该 Skill 的名称或使用
/rookie-memory触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v2.0.0
完整的进化版，支持L0永久记忆、bootstrap、autosave、混合检索、自动清理
元数据
常见问题
Rookie Memory 是什么？
Rookie-Memory 三级记忆管理系统 v2.0。专为 AI 代理设计的进化版记忆系统，包含 L0 永久记忆、L1 短期记忆、L2 中期记忆，支持 bootstrap 启动加载、autosave 自动保存、混合检索、自动清理等高级功能。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 283 次。
如何安装 Rookie Memory？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install rookie-memory」即可一键安装，无需额外配置。
Rookie Memory 是免费的吗？
是的，Rookie Memory 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Rookie Memory 支持哪些平台？
Rookie Memory 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Rookie Memory？
由 Rrrker（@rrrker）开发并维护，当前版本 v2.0.0。
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