Memory Vector v2.1 (多层知识库)

提供基于BGE-M3模型的自动日志蒸馏生成向量记忆，并支持语义相似度搜索与核心记忆自动更新功能。

Things to check before installing or running this skill: - Review and understand the two included JS files (dist/memory-distill.js and dist/memory-search.js) — they will read and write files under your current working directory (memory/*, memory/vector/*, MEMORY.md). - Expect to provide or configure embedding and LLM endpoints and API keys (EMBEDDING_URL, LLM_URL, EMBEDDING_API_KEY, LLM_API_KEY). The skill metadata does not declare these, but the scripts will use them; confirm endpoints are local/trusted (defaults point to http://localhost:11434). Do not supply production credentials until you audit the code. - The distill script contains a bug: it sets the Authorization header using the embedding API key even for LLM calls (CONFIG.embedding.apiKey is reused). If you plan to use a remote LLM service, update/fix the code so the LLM API key is used correctly. - The updateMemoryCore function writes a hard-coded personal block (address, commuting rules, names). This may be leftover sample data from the author — inspect and remove/replace these lines before running to avoid injecting unintended personal information into your MEMORY.md. - The provided distill file appears truncated in the supplied listing (the run call ends with a truncated .catch(console.er...), which could indicate an incomplete file; ensure the file is intact and syntactically correct before executing. - Run the scripts in an isolated test workspace (not your real data), confirm backups are created, and verify the regex-based 'filterSensitiveContent' is sufficient for your threat model — it's not comprehensive and can miss secrets. If you are not comfortable auditing or editing the code yourself, do not run this skill with sensitive logs or live credentials.