Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install memory-search或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install memory-search⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/memory-search/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
You have two tools for recalling information from your memory files. Use them.
memory_searchSemantic vector search across your indexed memory files (MEMORY.md, memory/*.md, and session transcripts).
Parameters:
| Param | Type | Required | Description | |---|---|---|---| | query | string | yes | Natural language question or topic to search for | | maxResults | number | no | Max results to return (default: 6) | | minScore | number | no | Minimum relevance score threshold (0-1) |
Example calls:
{ "query": "what projects is the human working on" }
{ "query": "preferences about code style", "maxResults": 3 }
{ "query": "important dates birthdays deadlines", "maxResults": 10, "minScore": 0.3 }
Returns: Array of results, each with:
snippet — the matching text chunkpath — relative file path (e.g. MEMORY.md, memory/2026-02-07.md)startLine / endLine — line range in the source filescore — relevance scorecitation — formatted source reference (in direct chats)memory_getRead a specific section of a memory file by path and line range. Use this after memory_search to pull more context around a result.
Parameters:
| Param | Type | Required | Description | |---|---|---|---| | path | string | yes | Relative path from workspace (e.g. MEMORY.md, memory/2026-02-07.md) | | from | number | no | Starting line number | | lines | number | no | Number of lines to read |
Example calls:
{ "path": "MEMORY.md" }
{ "path": "memory/2026-02-07.md", "from": 15, "lines": 30 }
Always search before answering about:
The pattern is:
memory_search with a relevant querymemory_get with the path and line rangeYour memory search covers:
MEMORY.md — your curated long-term memorymemory/*.md — daily notes and raw logsThese files are automatically indexed. You don't need to trigger indexing — just write to the files and the system handles the rest.
cat or ls to read memory files. Use memory_search and memory_get.memory/ directory looks sparse.安装 Memory Search 后,可以对 AI 说这些话来触发它
Help me get started with Memory Search
Explains what Memory Search does, walks through the setup, and runs a quick demo based on your current project
Use Memory Search to search and retrieve relevant information from your indexed memory f...
Invokes Memory Search with the right parameters and returns the result directly in the conversation
What can I do with Memory Search in my documents & notes workflow?
Lists the top use cases for Memory Search, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/memory-search/ 目录(个人级,所有项目可用),或 .claude/skills/memory-search/(项目级)。重启 AI 客户端后,用 /memory-search 主动调用,或让 AI 根据上下文自动发现并使用。
Memory Search 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
Memory Search 可免费安装使用。请查阅仓库了解许可证信息。
Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.
Memory Search 属于「Documents & Notes」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。
Automate my documents & notes tasks using Memory Search
Identifies repetitive steps in your workflow and sets up Memory Search to handle them automatically