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index1

AI memory system for coding agents — code index + cognitive facts, persistent across sessions.

gladego0 来源安装数☆ 0 仓库 Starsv2.0.3

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install gladego/index1

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

index1

AI memory system for coding agents with BM25 + vector hybrid search. Provides 6 MCP tools for intelligent code/doc search and cognitive fact recording.

What it does

  • Dual memory: corpus (code index) + cognition (episodic facts)
  • Hybrid search: BM25 full-text + vector semantic search with RRF fusion
  • Structure-aware chunking: Markdown, Python, Rust, JavaScript, plain text
  • MCP Server: 6 tools (recall, learn, read, status, reindex, config)
  • CJK optimized: Chinese/Japanese/Korean query detection with dynamic weight tuning
  • Built-in ONNX embedding: Vector search works out of the box, no Ollama required
  • Graceful degradation: Works without any embedding service (BM25-only mode)

Install

# Recommended
pipx install index1

# Or via pip
pip install index1

# Or via npm (auto-installs Python package)
npx index1@latest

One-click plugin setup:

index1 setup                 # Auto-configure hooks + MCP for Claude Code

Verify:

index1 --version
index1 doctor        # Check environment

Setup MCP

Create .mcp.json in your project root:

{
  "mcpServers": {
    "index1": {
      "type": "stdio",
      "command": "index1",
      "args": ["serve"]
    }
  }
}

If index1 is not in PATH, use the full path from which index1.

Add Search Rules

Add to your project's .claude/CLAUDE.md:

## Search Strategy

This project has index1 MCP Server configured (recall + 5 other tools). When searching code:

1. Known identifiers (function/class/file names) -> Grep/Glob directly (4ms)
2. Exploratory questions ("how does XX work") -> recall first, then Grep for details
3. CJK query for English code -> must use recall (Grep can't cross languages)
4. High-frequency keywords (50+ expected matches) -> prefer recall (saves 90%+ context)

Impact:

Without rules: Grep "search" -> 881 lines -> 35,895 tokens
With rules:    recall        -> 5 summaries ->...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/gladego/index1
Version
2.0.3
Commit
476241cab81f865fc87584c0e315a9d77a10c8a5
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# index1

AI memory system for coding agents with BM25 + vector hybrid search. Provides 6 MCP tools for intelligent code/doc search and cognitive fact recording.

## What it does

- **Dual memory**: corpus (code index) + cognition (episodic facts)
- **Hybrid search**: BM25 full-text + vector semantic search with RRF fusion
- **Structure-aware chunking**: Markdown, Python, Rust, JavaScript, plain text
- **MCP Server**: 6 tools (`recall`, `learn`, `read`, `status`, `reindex`, `config`)
- **CJK optimized**: Chinese/Japanese/Korean query detection with dynamic weight tuning
- **Built-in ONNX embedding**: Vector search works out of the box, no Ollama required
- **Graceful degradation**: Works without any embedding service (BM25-only mode)

## Install

```bash
# Recommended
pipx install index1

# Or via pip
pip install index1

# Or via npm (auto-installs Python package)
npx index1@latest
```

One-click plugin setup:

```bash
index1 setup                 # Auto-configure hooks + MCP for Claude Code
```

Verify:

```bash
index1 --version
index1 doctor        # Check environment
```

## Setup MCP

Create `.mcp.json` in your project root:

```json
{
  "mcpServers": {
    "index1": {
      "type": "stdio",
      "command": "index1",
      "args": ["serve"]
    }
  }
}
```

> If `index1` is not in PATH, use the full path from `which index1`.

## Add Search Rules

Add to your project's `.claude/CLAUDE.md`:

```markdown
## Search Strategy

This project has index1 MCP Server configured (recall + 5 other tools). When searching code:

1. Known identifiers (function/class/file names) -> Grep/Glob directly (4ms)
2. Exploratory questions ("how does XX work") -> recall first, then Grep for details
3. CJK query for English code -> must use recall (Grep can't cross languages)
4. High-frequency keywords (50+ expected matches) -> prefer recall (saves 90%+ context)
```

**Impact**:

```
Without rules: Grep "search" -> 881 lines -> 35,895 tokens
With rules:    recall        -> 5 summaries ->...

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