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: decision-tree

英語版ディレクトリ

Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption

14K
Stars
80/100
信頼
カテゴリ: agent-frameworks監査

FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.

27K
Stars
75/100
信頼
カテゴリ: finance監査

This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great.

13K
Stars
87/100
信頼
カテゴリ: design-creative監査

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

18K
Stars
87/100
信頼
カテゴリ: ml-automation監査

Create a structured post-earnings equity research update with key metrics, estimate changes, charts, and thesis review.

34K
Stars
75/100
信頼
カテゴリ: Finance監査

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

9.0K
Stars
86/100
信頼
カテゴリ: ml-automation監査

Turn any technical book PDF into a Claude Code skill — ready to study, reference, and use while you work.

1.0K
Stars
85/100
信頼
カテゴリ: utility監査

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

4.7K
Stars
76/100
信頼
カテゴリ: ml-automation監査

Superfast AI decision making and intelligent processing of multi-modal data.

3.6K
Stars
76/100
信頼
カテゴリ: robotics-iot監査
Sem84

Semantic version control => entity-level diffs, blame, and impact analysis on top of git. 26 languages via tree-sitter. Built for coding agents.

3.2K
Stars
84/100
信頼
カテゴリ: agent-frameworks監査

A curated collection of 13 tested Claude Code agent skills for producing decks, research briefs, PRDs, articles, audits, and other finished work.

626
Stars
83/100
信頼
カテゴリ: presentation監査

Crabbox: warm a box, sync the diff, run the suite.

1.3K
Stars
81/100
信頼
カテゴリ: utility監査