brainlayer — for Claude Code brainlayer, golems, community, for Claude Code, ide skills, brain_search, brain_store, brain_recall, Knowledge, Pipeline

v1.0.0

关于此技能

非常适合需要高级对话历史分析和向量数据库查询功能的AI代理。 本地化技能摘要: Use when brain search/brain store/brain recall fail, when you need BrainLayer CLI syntax for indexing/enrichment, or when checking storage stats and DB paths. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

功能特性

BrainLayer — Knowledge Pipeline
External repo: github.com/EtanHey/brainlayer
Formerly "Zikaron". CLI: brainlayer. MCP tools: brain search, brain store, brain recall.
MCP Tools (3 available)
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# 核心主题

EtanHey EtanHey
[0]
[0]
更新于: 3/12/2026

技能概览

先看适用场景、限制条件和安装路径,再决定是否继续深入。

非常适合需要高级对话历史分析和向量数据库查询功能的AI代理。 本地化技能摘要: Use when brain search/brain store/brain recall fail, when you need BrainLayer CLI syntax for indexing/enrichment, or when checking storage stats and DB paths. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

核心价值

赋予代理执行语义搜索的能力,跨越过去的对话和知识,使用如brain_search、brain_store和brain_recall等工具,利用可搜索的向量数据库通过brainlayer CLI查询过去的解决方案、代码模式和调试会话。

适用 Agent 类型

非常适合需要高级对话历史分析和向量数据库查询功能的AI代理。

赋予的主要能力 · brainlayer

查询过去的对话以获取代码模式和解决方案
将Claude Code会话历史记录索引到可搜索的向量数据库中
使用brain_recall进行高效的问题解决调试会话

! 使用限制与门槛

  • 需要安装brainlayer CLI
  • 仅限于索引来自支持的平台(如Claude Code)的会话历史记录
  • 依赖于Golem生态系统monorepo

关于来源内容

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

brainlayer 是什么?

非常适合需要高级对话历史分析和向量数据库查询功能的AI代理。 本地化技能摘要: Use when brain search/brain store/brain recall fail, when you need BrainLayer CLI syntax for indexing/enrichment, or when checking storage stats and DB paths. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

如何安装 brainlayer?

运行命令:npx killer-skills add EtanHey/golems/brainlayer。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

brainlayer 适用于哪些场景?

典型场景包括:查询过去的对话以获取代码模式和解决方案、将Claude Code会话历史记录索引到可搜索的向量数据库中、使用brain_recall进行高效的问题解决调试会话。

brainlayer 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

brainlayer 有哪些限制?

需要安装brainlayer CLI;仅限于索引来自支持的平台(如Claude Code)的会话历史记录;依赖于Golem生态系统monorepo。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add EtanHey/golems/brainlayer。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    brainlayer 已启用,可立即在当前项目中调用。

! 来源说明

此页面仍可作为安装与查阅参考。继续使用前,请结合上方适用场景、限制条件和上游仓库说明一起判断。

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

brainlayer

安装 brainlayer,这是一款面向AI agent workflows and automation的 AI Agent Skill。查看功能、使用场景、限制条件与安装命令。

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

BrainLayer — Knowledge Pipeline

External repo: github.com/EtanHey/brainlayer Formerly "Zikaron". CLI: brainlayer. MCP tools: brain_search, brain_store, brain_recall.

BrainLayer indexes Claude Code conversation history into a searchable vector database. Query past solutions, code patterns, and debugging sessions.

MCP Tools (3 available)

ToolWhat It Does
brain_searchSemantic search across past conversations and knowledge
brain_storePersistently store memories (auto-type, auto-importance)
brain_recallProactive context retrieval by file, topic, or current state

Old brainlayer_* names still work as backward-compat aliases.

CLI Quick Reference

bash
1# Search past conversations 2brainlayer search "how did I implement authentication" 3brainlayer search "error handling" --project golems 4 5# Index conversations 6brainlayer index 7brainlayer index -p project-name 8 9# Run enrichment (local LLM metadata extraction) 10brainlayer enrich 11 12# Stats 13brainlayer stats 14 15# Interactive dashboard 16brainlayer dashboard

MCP Config

json
1{ 2 "mcpServers": { 3 "brainlayer": { 4 "command": "brainlayer-mcp" 5 } 6 } 7}

Storage

text
1~/.local/share/brainlayer/ 2├── brainlayer.db # sqlite-vec database (vectors + metadata, ~1.4GB) 3└── prompts/ # Deduplicated system prompts

When to Use

  • Finding past solutions: "How did I handle that API rate limiting before?"
  • Debugging patterns: "What error messages have I seen with this library?"
  • Code reuse: "Find my previous implementation of pagination"
  • File history: "What sessions touched this file?"
  • Regression detection: "What changed since this file last worked?"

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