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: kaggle

英語版ディレクトリ

🏅 Collection of Kaggle Solutions and Ideas 🏅

6.4K
Stars
81/100
信頼
カテゴリ: ml-automation監査

FLUX, Stable Diffusion, SDXL, SD3, LoRA, Fine Tuning, DreamBooth, Training, Automatic1111, Forge WebUI, SwarmUI, DeepFake, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, Kaggle, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya, Midjourney, RunPod

2.7K
Stars
85/100
信頼
カテゴリ: education監査

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

29K
Stars
70/100
信頼
カテゴリ: ml-automation監査

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

603
Stars
73/100
信頼
カテゴリ: rag-knowledge監査

1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.

1.8K
Stars
68/100
信頼
カテゴリ: rag-knowledge監査

goto_conversion - Powered $47,000 of prize money, 10+ Gold Medals and 100+ Medals on Kaggle

111
Stars
69/100
信頼
カテゴリ: sports-analytics監査

A Python library for downloading datasets from Kaggle, Google Drive, and other online sources.

346
Stars
67/100
信頼
カテゴリ: ml-automation監査

The First Place Solution of Kaggle iMaterialist (Fashion) 2019 at FGVC6

484
Stars
61/100
信頼
カテゴリ: robotics-iot監査

Code for Kaggle and Offline Competitions

291
Stars
60/100
信頼
カテゴリ: ml-automation監査