Skill 디렉토리

AI Agent를 위한 재사용 가능한 Skill을 찾으세요.

작업으로 실제 GitHub Skill을 검색하고 사용 전에 Stars, 신뢰, 감사, 카테고리, 설치 경로를 확인하세요.

모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.

검색 결과: prediction-markets

영문 디렉토리

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
신뢰
카테고리: research감사

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
신뢰
카테고리: finance감사

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

10K
Stars
82/100
신뢰
카테고리: finance감사

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

7.9K
Stars
79/100
신뢰
카테고리: finance감사

Message Passing Neural Networks for Molecule Property Prediction

2.4K
Stars
74/100
신뢰
카테고리: ml-automation감사

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
신뢰
카테고리: research감사

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

2.0K
Stars
77/100
신뢰
카테고리: finance감사

Natural Gradient Boosting for Probabilistic Prediction

1.9K
Stars
80/100
신뢰
카테고리: ml-automation감사
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
신뢰
카테고리: media-automation감사

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

2.3K
Stars
83/100
신뢰
카테고리: agent-frameworks감사

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
신뢰
카테고리: research감사

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
신뢰
카테고리: design-creative감사