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: failure-prediction

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

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

A portable agent skill that makes AI agents research comparable projects, tradeoffs, costs, and failure conditions before giving build advice.

176
Stars
77/100
信頼
カテゴリ: productivity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

JD-driven Chinese resume optimization skill for job seekers, packaged as an installable AI agent skill with prompts and test cases.

135
Stars
75/100
信頼
カテゴリ: utility監査

A meta-skill that creates, evaluates, and improves other AI agent skills with multiple modes and evidence-based validation.

133
Stars
77/100
信頼
カテゴリ: utility監査

Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectural extension point that adds or expands shared surface area, especially when consumers are absent, exports are unused or speculative, existing consumers are hack-heavy, forward compatibility is claimed, or multiple similar APIs may express one demand.

51K
Stars
78/100
信頼
カテゴリ: research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