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 gnosis · Agent Tool · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is prediction-market-agent-tooling safe to install? View the security audit →
Prediction Market Agent Tooling Tooling for benchmarking, deploying and monitoring agents for prediction market applications. Setup Install the project dependencies with , using Python =3.10: Create a file in the root of the repo with the following variables: Deploying and monitoring agents using GCP requires that you set up the gcloud CLI (see here for installation instructions, and use to authorize.) Benchmarking Create a benchmarkable agent by subclassing the base class, and plug in your agent's research and prediction functions into the method. Use the class to compare your agent's predictions vs. the 'wisdom of the crowd' on a set of markets from your chosen prediction market platform. For example: python import predictionmarketagenttooling.benchmark.benchmark as bm from predictionmarketagenttooling.benchmark.agents import RandomAgent from predictionmarketagenttooling.markets.markettype import MarketType from predictionmarketagenttooling.markets.markets import getbinarymarkets benchmarker = bm.Benchmarker( markets=getbinarymarkets(limit=10, markettype=MarketType.MANIFOLD), agents=[RandomAgent(agentname="arandomagen
| Stars | 57 |
| Forks | 16 |
| Language | Python |
| Category | Agent Tool |
| License | LGPL-3.0 |
| Quality Score | 71.5111657930856/100 |
| Open Issues | 118 |
| Last Updated | 2026-04-22 |
| Created | 2024-02-08 |
| Platforms | browser, python |
| Est. Tokens | ~733k |
These tools work well together with prediction-market-agent-tooling for enhanced workflows:
Looking for a prediction-market-agent-tooling alternative? If you're comparing prediction-market-agent-tooling with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Code search MCP for Claude Code. Make entire codebase the context for any coding agent. Embeddings are created
Your own Claude Code UI, sandbox, in-browser VS Code, terminal, multi-provider support (Anthropic, OpenAI, Git
Polymarket AI market suggestor that blends real-time news, social sentiment, and on-chain trends to propose ne
MCP server for Claude Code/VSCode/Cursor/Windsurf to use editor self functionality. ⚡ Get real-time LSP diagno
Free crypto news API - real-time aggregator for Bitcoin, Ethereum, DeFi, Solana & altcoins. No API key require
The brutally honest map of where AI-agent money actually flows. Every number measured, dated, and re-checked —
Explore other popular agent tool tools:
prediction-market-agent-tooling is Tools to benchmark, deploy and monitor prediction market agents.. It is categorized as a Agent Tool with 57 GitHub stars.
prediction-market-agent-tooling is primarily written in Python. It covers topics such as agent, gnosis, prediction-market.
You can find installation instructions and usage details in the prediction-market-agent-tooling GitHub repository at github.com/gnosis/prediction-market-agent-tooling. The project has 57 stars and 16 forks, indicating an active community.
prediction-market-agent-tooling is released under the LGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to prediction-market-agent-tooling on Agent Skills Hub include claude-context-local, claudex, polymarket-ai-market-suggestor. 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: