本地向量记忆工具，替代内置 memory 工具。使用 ChromaDB + BGE-small-zh 实现完全离线的语义记忆存储和检索。使用场景：(1) 存储重要信息到长期记忆，(2) 语义搜索历史记忆，(3) 删除特定记忆。触发词：记住、记忆、recall、memory、forget。

This skill appears to implement a local semantic memory using ChromaDB and a Chinese BGE model, and the scripts act only on a local data/ folder. However, before installing or running it: (1) inspect or ask about the HF mirror (https://hf-mirror.com) — the scripts force model downloads from that endpoint instead of the usual Hugging Face hosts; an untrusted mirror could supply tampered models. (2) Run setup in an isolated environment (virtualenv / container) because it runs pip installs (no version pins) and downloads models. (3) If you truly require offline operation, skip running setup and supply the model files locally and remove or override the HF_ENDPOINT lines in the scripts. (4) Prefer pinning package versions or reviewing pip install outputs. If you cannot validate the mirror or are uncomfortable with remote downloads, treat this as risky and do not run setup on sensitive machines.

Type: OpenClaw Skill Name: local-memory Version: 1.0.0 The skill bundle provides a legitimate local vector memory system using ChromaDB and the BGE-small-zh-v1.5 embedding model. The scripts (memory_store.py, memory_recall.py, memory_forget.py) implement standard semantic storage and retrieval logic, while setup.py handles dependency installation and model downloading. No evidence of data exfiltration, malicious execution, or prompt injection was found; the use of a Hugging Face mirror (hf-mirror.com) is a common practice for users in restricted network environments.