Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
index1
AI memory system for coding agents — code index + cognitive facts, persistent across sessions.
skill openclawclawhub Free
AI memory system for coding agents — code index + cognitive facts, persistent across sessions.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.3
Provenance
- Publisher
- gladego
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 -> 460 tokens (97% savings)
Index Your Project
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: 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
index1 web # Start Web UI on port 6888 index1 web --port 8080 # Custom port
MCP Tools Reference
ToolDescriptionrecallUnified search — code + cognitive facts, BM25 + vector hybridlearnRecord insights, decisions, lessons learned (auto-classify + dedup)readRead file content + index metadatastatusIndex and cognition statisticsreindexRebuild index for a path or collectionconfigView or modify configuration
Troubleshooting
IssueFixTools not showingCheck .mcp.json format and index1 pathAI doesn't use recallAdd search rules to CLAUDE.mdcommand not foundUse full path from which index1Chinese search returns 0Install Ollama + bge-m3 modelNo vector searchBuilt-in ONNX should work; run index1 doctor
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md