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: segmentation-refinement

英語版ディレクトリ

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

13K
Stars
87/100
信頼
カテゴリ: ml-automation監査

Generate draw.io diagrams from natural language — 6 presets, vision self-check + up to 5-round refinement, codebase-to-diagram, 10,000+ official shapes & 321 AI/LLM brand logos. Exports PNG/SVG/PDF/JPG.

7.4K
Stars
86/100
信頼
カテゴリ: agent-skills監査

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

7.8K
Stars
77/100
信頼
カテゴリ: ml-automation監査

RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]

7.8K
Stars
86/100
信頼
カテゴリ: ml-automation監査

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

2.1K
Stars
85/100
信頼
カテゴリ: ml-automation監査

Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.

907
Stars
69/100
信頼
カテゴリ: data-analysis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

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

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

6.2K
Stars
75/100
信頼
カテゴリ: ml-automation監査

[ECCV 2022] This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

4.5K
Stars
72/100
信頼
カテゴリ: robotics-iot監査

[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation

3.5K
Stars
74/100
信頼
カテゴリ: robotics-iot監査