A comprehensive MCP-powered toolkit for building, optimizing, and deploying automated trading strategies across crypto and prediction markets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install robonet-workbench
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install robonet-workbench using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Robonet Workbench?
Robonet Workbench is an advanced MCP server integration that empowers AI agents to handle the entire lifecycle of trading strategy development. By providing 24 specialized tools, it allows developers and traders to browse market data, generate Python-based strategies using the Jesse framework, and conduct rigorous backtesting. This Openclaw Skills integration bridges the gap between AI-driven strategy ideation and real-world execution on platforms like Hyperliquid and Polymarket.
The skill is designed for high-performance financial engineering, offering access to over 170 technical indicators and sophisticated machine learning predictions via the Allora Network. Whether you are a quant developer looking to optimize parameters or a trader seeking to automate event-based predictions, Robonet Workbench provides a professional-grade infrastructure for modern algorithmic trading.
Robonet Workbench Use Cases
- Generating complete Python trading strategies from natural language descriptions.
- Backtesting crypto and prediction market strategies on historical data to evaluate Sharpe ratios and drawdowns.
- Enhancing existing trading bots with machine learning price predictions from the Allora Network.
- Deploying live trading agents to Hyperliquid via EOA or Vault architectures.
- Optimizing strategy parameters like RSI periods or stop-loss thresholds for maximum profitability.
How Robonet Workbench Works
- Resource Discovery: The agent uses data tools to identify available trading pairs, technical indicators, and data ranges.
- Strategy Generation: AI-powered tools generate or refine Python code following the Jesse trading framework standards.
- Historical Backtesting: The strategy is tested against historical market data to generate detailed performance metrics and trade statistics.
- ML Enhancement: Users can optionally integrate Allora Network predictions to add a machine learning layer to the strategy's entry/exit logic.
- Live Deployment: Validated strategies are deployed to live environments for automated execution on decentralized exchanges.
Robonet Workbench Setup
To initialize the Robonet Workbench within your environment, ensure you have a valid API key from the Robonet dashboard. Load the necessary tools using your MCP-compatible assistant:
# Load core strategy development tools
Use MCPSearch to select: mcp__workbench__get_all_symbols
Use MCPSearch to select: mcp__workbench__create_strategy
Use MCPSearch to select: mcp__workbench__run_backtest
Ensure your ROBONET_API_KEY is configured in your environment variables to facilitate authenticated requests and credit management for AI operations.
Robonet Workbench Data Schema & Taxonomy
The skill organizes its operations through a structured data model centered on strategies and market metrics.
| Component | Data Organized | Key Metadata |
|---|---|---|
| Strategies | Jesse Framework Python Code | Name, Risk Level (H/M/L), Version |
| Market Data | Symbols & Technical Indicators | OHLCV, 170+ indicators, Data coverage |
| Performance | Backtest Results | Sharpe ratio, Profit factor, Max drawdown |
| ML Signals | Allora Network Predictions | Topic ID, Time horizon, Prediction type |
| Deployment | Live Trading State | EOA/Vault ID, Active status, Leverage |
Robonet Workbench Advanced Features
- Integration with Allora Network for multi-horizon ML price predictions (5m to 1 week).
- Specialized support for Polymarket YES/NO prediction market trading strategies.
- Parameter optimization tools that automatically tune strategy variables for better historical performance.
- Dual deployment modes: EOA for simple wallet-based trading and Hyperliquid Vaults for professional-grade, multi-deployment setups.
- Comprehensive credit management system to track and control AI generation costs in real-time.
SKILL.md
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