A sophisticated prediction market betting engine designed to identify edge and optimize stakes using the Kelly criterion.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install argus-edge
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 argus-edge 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 Argus Edge?
Argus Edge is a specialized betting strategy and analytical engine designed for Openclaw Skills users targeting crypto prediction markets like Polymarket. It bridges the gap between technical analysis and market odds by computing technical analysis (TA)-implied probabilities against current market pricing to detect profitable edge.
This skill for Openclaw Skills empowers traders to move beyond intuition, utilizing a battle-tested framework derived from over 100 live bets to execute disciplined, data-driven betting strategies. By integrating real-time market data with calibrated TA scores, it provides a high-probability roadmap for prediction market participation.
Argus Edge Use Cases
- Automated edge detection in Polymarket betting windows.
- Calculating expected value (EV) for specific asset price predictions including BTC, ETH, SOL, and XRP.
- Applying the Kelly criterion for optimal bankroll management and stake sizing.
- Identifying high-payout counter-consensus opportunities when market sentiment diverges from technical signals.
- Filtering for high-probability "fresh" markets within their first 30 minutes of activity to maximize win rates.
How Argus Edge Works
- The agent scans active prediction markets to ingest current market-implied win probabilities.
- It applies asset-specific TA calibration to determine a private win probability based on technical indicators and historical reliability.
- The system calculates the numerical edge by subtracting market probability from the TA-implied probability.
- It validates the opportunity against strict freshness guards and technical score thresholds, specifically looking for an edge greater than 10%.
- Finally, it generates an optimized stake size using the Kelly criterion relative to the user's defined bankroll to manage risk and maximize growth.
Argus Edge Setup
To integrate this betting engine into your Openclaw Skills environment, follow these steps to initialize the strategy parameters.
# Install the argus-edge skill to your local environment
openclaw install argus-edge
# Configure your betting parameters and bankroll
openclaw config argus-edge --bankroll 1000 --min-edge 0.10
Argus Edge Data Schema & Taxonomy
The skill organizes data around market signals, asset reliability, and historical performance lessons to ensure Openclaw Skills users have high-fidelity data.
| Component | Data Type | Description |
|---|---|---|
| TA Reliability | Float | Reliability coefficient per asset (e.g., 0.90 for SOL, 0.75 for BTC). |
| Freshness Guard | Boolean | Logic flag for markets less than 30 minutes old. |
| Kelly Stake | Formula | Calculation based on (edge × bankroll) / odds. |
| Strategy Lessons | Metadata | Collection of 25 documented lessons (L001–L025) for guardrail logic. |
Argus Edge Advanced Features
- Asset Calibration: Pre-configured bias and reliability settings tailored for major crypto assets like SOL, ETH, and BTC.
- Counter-Consensus Rule (L023): Automated identification of positive EV opportunities in heavily one-sided markets where consensus is wrong.
- Dynamic Stake Sizing: Real-time Kelly criterion adjustments based on shifting market odds and technical confidence.
- Multi-Agent Support: Can be paired with other Openclaw Skills to trigger automated execution or deeper sentiment research.
- Consensus Filtering: Automatically skips dead signals in markets with >92% consensus to avoid low-reward scenarios.
SKILL.md
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