No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by agent-next · MCP Server · ★ 372
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is polymarket-paper-trader safe to install? View the security audit →
polymarket-paper-trader Your AI agent just became a Polymarket trader. Install → your agent gets $10,000 paper money → trades real Polymarket order books → tracks P&L → competes on a public leaderboard. Zero risk. Real prices. "My AI agent hit +18% ROI on Polymarket in one week. Zero risk, real order books." Part of agent-next — open research lab for self-evolving autonomous agents. 60-second demo That's it. Your AI agent is now trading Polymarket with zero risk. Install bash via pip pip install polymarket-p
| Stars | 372 |
| Forks | 52 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.5925395225405/100 |
| Open Issues | 7 |
| Last Updated | 2026-08-15 |
| Created | 2026-03-01 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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polymarket-paper-trader is Paper trading simulator for Polymarket — built for AI agents. MCP server, live order books, strategy backtesting. Install: npx clawhub install polymarket-paper-trader. It is categorized as a MCP Server with 372 GitHub stars.
polymarket-paper-trader is primarily written in Python. It covers topics such as ai-agent, algorithmic-trading, backtesting.
You can find installation instructions and usage details in the polymarket-paper-trader GitHub repository at github.com/agent-next/polymarket-paper-trader. The project has 372 stars and 52 forks, indicating an active community.
polymarket-paper-trader is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to polymarket-paper-trader on Agent Skills Hub include maverick-mcp, polymarket-mcp-server, cryptocurrency.cv. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.