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

v1.0.0
GitHub

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)
------ -------------

# 核心主题

for Claude Code brain_search brain_store brain_recall Knowledge Pipeline
EtanHey EtanHey
[0]
[0]
更新于: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 9/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
9/11
Quality Score
57
Canonical Locale
en
Detected Body Locale
en

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

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

评审后的下一步

先决定动作，再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”，而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核，以及是否已经进入工作流落地阶段。

安装路径

先进入安装与验证

如果这项技能值得继续，下一步应该先确认安装命令、CLI 写入路径与环境验证。
可信复核

回到可信合集再做一次复核

如果你还在比较不同技能或供应方，先回到可信合集做二次判断，而不是继续放大仓库噪音。
工作流

如果要进团队流转，转到工作流集合

当你的目标已经不是单个技能，而是团队协作、审批和可重复执行，就应该转到工作流合集继续比较。
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。

! 参考页模式

此页面仍可作为安装与查阅参考，但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论，再决定是否继续查看上游仓库说明。

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

brainlayer

安装 brainlayer，这是一款面向AI agent workflows and automation的 AI Agent Skill。查看评审结论、使用场景与安装路径。

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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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