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技能档案

Memory

Long-term semantic memory using Qdrant vector database. Store and search information across conversations using vector similarity.

JoshuaCastanedaAH0 星标2026年3月30日

职业
分类: 生物信息学

技能内容

When to Use

User asks to remember something for later
User wants to recall previously stored information
User needs semantic search across stored knowledge
User says "remember that...", "what do you know about...", "save this for later"

Instructions

Storing Memories

Use memory_store to save information with optional metadata:

Store key decisions, preferences, or facts
Include relevant metadata (category, source, date) for better retrieval
Keep content focused — one concept per memory for better search results

Searching Memories

Use memory_search for semantic similarity search:

Queries are matched by meaning, not exact keywords
Default returns top 5 results — adjust limit for more or fewer
Results include similarity scores — higher is more relevant

相关技能

快速安装

Memory

npx skills add JoshuaCastanedaAH/sean-mcp-servers

下载 Skill 打开源码仓库

作者: JoshuaCastanedaAH
星标: 0
更新时间: 2026年3月30日
职业

本页内容

Falls back to hash-based pseudo-embeddings if no embedding API is configured

Best Practices

Store information when the user explicitly asks or when context is important for future conversations
Use descriptive content — "User prefers Python for backend development" is better than "Python"
Add metadata tags to categorize memories (e.g., { "category": "preference" })

Tools

memory_store — Save content with optional metadata to long-term memory
memory_search — Search memories using semantic similarity (returns ranked results with scores)

02

Instructions

03Storing Memories

04Searching Memories

05Best Practices

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