Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: prediction-markets

英語版ディレクトリ

Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.

54K
Stars
83/100
信頼
カテゴリ: research監査

LLM驱动的 A/H/美股智能分析:多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送,零成本定时运行,纯白嫖. LLM-powered stock analysis system for A/H/US markets.

52K
Stars
82/100
信頼
カテゴリ: finance監査

Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).

10K
Stars
82/100
信頼
カテゴリ: finance監査

This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.

7.9K
Stars
79/100
信頼
カテゴリ: finance監査

Message Passing Neural Networks for Molecule Property Prediction

2.4K
Stars
74/100
信頼
カテゴリ: ml-automation監査

MiroThinker is a deep research agent optimized for complex research and prediction tasks. Our latest models, MiroThinker-1.7, achieves 74.0 and 75.3 on the BrowseComp and BrowseComp Zh, respectively.

8.3K
Stars
84/100
信頼
カテゴリ: research監査

CCXT for prediction markets. PMXT is a unified API for trading on Polymarket, Kalshi, and more.

2.0K
Stars
77/100
信頼
カテゴリ: finance監査

Natural Gradient Boosting for Probabilistic Prediction

1.9K
Stars
80/100
信頼
カテゴリ: ml-automation監査
VAR83

[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!

8.7K
Stars
83/100
信頼
カテゴリ: media-automation監査

Offline multi-agent simulation & prediction engine. English fork of MiroFish with Neo4j + Ollama local stack.

2.3K
Stars
83/100
信頼
カテゴリ: agent-frameworks監査

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

34K
Stars
80/100
信頼
カテゴリ: research監査

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

25K
Stars
77/100
信頼
カテゴリ: design-creative監査