Memory Focal System

焦距记忆系统 - 智能记忆管理，支持消息分类、按需加载、自动标签、遗忘曲线。Token 优化 40-60%。

This package appears to implement a local memory manager and classifier (no network exfiltration is visible), but there are a few red flags you should address before enabling it: 1) Inspect the workspace path it writes to (~/.openclaw/workspace/memory-focal/) and confirm you are comfortable with conversation data being appended to data/raw/buffer.jsonl; disable auto_write in config.json if you want manual control. 2) Verify there are no unexpected files in ~/.openclaw/workspace/memory-focal/scripts — memory_manager adds that directory to Python's import path, which could cause it to import untrusted local modules. 3) Know that SKILL.md/README reference many scripts and features that are not present in the packaged files — ask the maintainer or check the GitHub repo for the full release before trusting advertised capabilities (e.g., auto-tagging, frequency trigger, forget-curve). 4) If you plan to enable LLM auto-tagging, only provide API keys to trusted providers and confirm the code that will use them. If you want stronger assurance, request the missing scripts or a complete release and re-run a code review; otherwise consider running the skill in a sandboxed environment and set "auto_write": false first.

Type: OpenClaw Skill Name: memory-focal-system Version: 1.0.0 The memory-focal-system is a utility designed to manage AI context and long-term memory through tiered local storage and keyword-based classification. The provided Python files (memory_manager.py and classifier.py) implement standard file I/O operations within the expected workspace directory (~/.openclaw/workspace/memory-focal) and lack any indicators of malicious intent, such as data exfiltration, unauthorized network calls, or command execution. The logic is transparent, well-documented, and aligns entirely with the stated purpose of optimizing token usage.