Overview
- Skill Key
- gladego/index1
- Author
- gladego
- Source Repo
- openclaw/skills
- Version
- 2.0.3
- Source Path
- skills/gladego/index1
- Latest Commit SHA
- 476241cab81f865fc87584c0e315a9d77a10c8a5
AI memory system for coding agents — code index + cognitive facts, persistent across sessions.
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0
Installs
0
Status
ACTIVE
Visibility
PUBLIC
直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 index1 技能。 若已安装,则直接安装 index1 技能。
# 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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