Skill: gladego/index1 (version 2.0.3)

--- name: index1 description: AI memory system for coding agents — code index + cognitive facts, persistent across sessions. version: 2.0.3 license: Apache-2.0 author: gladego tags: [mcp, memory, semantic-search, bm25, rag, cognitive, coding-agent] --- # 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 -> 460 tokens (97% savings) ``` ## Index Your Project ```bash index1 index ./src ./docs # Index source and docs index1 status # Check index stats index1 search "your query" # Test search ``` ## Optional: Multilingual Enhancement index1 v2 has built-in ONNX embedding (bge-small-en-v1.5). For better multilingual support: ```bash curl -fsSL https://ollama.com/install.sh | sh ollama pull nomic-embed-text # Standard, 270MB # or ollama pull bge-m3 # Best for CJK, 1.2GB index1 config embed_backend ollama index1 doctor # Verify setup ``` Without Ollama, ONNX embedding provides vector search out of the box. ## Web UI ```bash index1 web # Start Web UI on port 6888 index1 web --port 8080 # Custom port ``` ## MCP Tools Reference | Tool | Description | |------|-------------| | `recall` | Unified search — code + cognitive facts, BM25 + vector hybrid | | `learn` | Record insights, decisions, lessons learned (auto-classify + dedup) | | `read` | Read file content + index metadata | | `status` | Index and cognition statistics | | `reindex` | Rebuild index for a path or collection | | `config` | View or modify configuration | ## Troubleshooting | Issue | Fix | |-------|-----| | Tools not showing | Check `.mcp.json` format and `index1` path | | AI doesn't use recall | Add search rules to CLAUDE.md | | `command not found` | Use full path from `which index1` | | Chinese search returns 0 | Install Ollama + `bge-m3` model | | No vector search | Built-in ONNX should work; run `index1 doctor` |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)