微信收藏知识库

微信收藏夹导出、智能分类与知识库管理。支持从解析后的 favorite.db 导出收藏记录、三级分类体系（一级9类 + 二级57标签 + 跨领域6类）、LLM 智能增强（可选）、批量导入 IMA 知识库（可选）。核心功能支持离线使用，网络功能默认关闭。

This skill appears to do what it says: local decryption/export/classification of WeChat favorites, with optional LLM classification and optional bulk import to Tencent IMA. Before installing/running, consider: 1) Data sensitivity — decrypt_db will attempt to decrypt local WeChat DB files if you supply a keys file (all_keys.json); ensure that all_keys.json was created by you with a trusted tool and that you are comfortable decrypting the files on this machine. 2) Auto-detection — config.py may auto-detect WeChat data directories and will write config.json to the script directory; if you prefer manual control, create config.json yourself. 3) Network/credentials are optional — set SAFE_MODE=1 to force offline operation; if you enable LLM or IMA, provide LLM_API_KEY / IMA credentials only in trusted environments and understand which fields are sent (LLM code builds prompts containing title/source/url; import_ima sends URL lists to ima.qq.com). 4) Code quality notes: import_ima.py contains a bug (uses undefined variable batch_count) which may raise an exception; llm and incremental scripts expect LLM_API_KEY for network usage. 5) Least privilege: run in an isolated/trusted environment and review/verify config and credential files (~/.config/ima, environment variables) before use. If you want greater assurance, run the scripts on a copy of your data or inspect the few omitted helper files (e.g., find_all_keys.py if you plan to extract keys) so you understand how keys are produced.