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 YichengYang-Ethan · MCP Server · ★ 258
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is oracle3-prediction-market-agent safe to install? View the security audit →
Oracle3 Oracle3 is an open-source paper-trading engine and MCP server for prediction markets. It maps logical relations between event contracts on Kalshi and Polymarket, checks whether quoted prices break the axioms of probability after each venue's fees, and paper-trades the baskets that survive under pre-trade risk limits. Status: paper-traded research software with no live track record. The MCP server cannot place real orders. See What is verified, and what is not? At a glance | Agent int
| Stars | 258 |
| Forks | 36 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.957255586308/100 |
| Open Issues | 15 |
| Last Updated | 2026-09-30 |
| Created | 2026-02-28 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~16k |
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oracle3-prediction-market-agent is Oracle3: open-source trading engine and MCP server for prediction markets. Finds fee-adjusted no-arbitrage violations across related event contracts and trades them live on Kalshi, Polymarket and Sola. It is categorized as a MCP Server with 258 GitHub stars.
oracle3-prediction-market-agent is primarily written in Python. It covers topics such as agentic-ai, ai-agents, algorithmic-trading.
You can find installation instructions and usage details in the oracle3-prediction-market-agent GitHub repository at github.com/YichengYang-Ethan/oracle3-prediction-market-agent. The project has 258 stars and 36 forks, indicating an active community.
oracle3-prediction-market-agent is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to oracle3-prediction-market-agent on Agent Skills Hub include polymarket-paper-trader, maverick-mcp, ai-trader. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: