Homebutler: OpenClaw Skill for Homelab Management via Telegram
Homebutler is a Go binary that functions as an OpenClaw skill, allowing developers to manage homelab infrastructure directly from Telegram chat. The tool addresses the pain point of needing multiple SSH sessions for maintenance, particularly during off-hours.
Key Features
- System monitoring: CPU, memory, disk usage, and uptime tracking
- Docker management: List containers, restart, stop, and view logs
- Multi-server SSH with parallel execution capability
- Wake-on-LAN functionality
- Port scanner and network scanning
- Threshold-based alerts for resource spikes
- Web dashboard accessible via
homebutler serve - MCP server compatibility for Claude, ChatGPT, and Cursor
Setup and Usage
Installation requires a single command: clawhub install homeserver. Once installed, the claw can execute commands through natural language in Telegram.
Example interaction from the source:
🚨 Alert: CPU at 92% on nas-box Me: "What's eating CPU?" Claw: postgres — 85.2% Me: "Restart postgres" Claw: ✅ Done. CPU 92% → 12%
The tool is designed for developers running homelabs with multiple devices (Mac Mini, Raspberry Pi, NAS) who want to reduce operational overhead.
📖 Read the full source: r/openclaw
👀 See Also
Free pharmacovigilance signal detection tool built with Claude Code
A developer used Claude Code to build a free pharmacovigilance platform that analyzes 2.9 million FDA adverse event reports, running statistical signal detection that commercial platforms charge $50K-500K/year for. The tool is hosted for free on HuggingFace.
Otterly: Route OpenClaw Through Your Claude Code Subscription
Otterly is a small npm package that exposes the local Claude CLI as an OpenAI-compatible HTTP server, letting you bill OpenClaw requests to your Claude Code subscription instead of pay-per-token API rates.
LLM Circuit Finder: Duplicate 3 layers to boost reasoning without training
A new toolkit finds 'reasoning circuits' in transformer models - contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicating these blocks (layers 12-14 in Devstral-24B) improves logical deduction from 0.22 to 0.76 on BBH benchmarks with no weight changes or training.
Cowork vs. Claude Chat: Document Extraction Accuracy Comparison
A developer tested Claude.ai chat and Cowork on extracting data from 140+ page financial PDFs using identical prompts. Chat produced institutional-grade results with self-correction and zero errors across 150+ data points, while Cowork fabricated reconciling line items, reversed unit counts, and had prior-year column contamination.