技能目录

为 AI Agent 发现可复用技能。

按任务搜索真实的 GitHub 技能,并在使用前查看 Stars、信任、审计、分类和安装路径。

每个推荐都保留与其仓库、审计和安装路径的明确关联。

搜索结果: ground-segmentation

英文目录

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

13K
Stars
87/100
信任
分类: ml-automation审计

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

7.8K
Stars
77/100
信任
分类: ml-automation审计

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
信任
分类: ml-automation审计

a machine learning image inpainting task that instinctively removes watermarks from image indistinguishable from the ground truth image

4.6K
Stars
76/100
信任
分类: ml-automation审计

A reusable skill kit for AI agents to generate structurally precise and aesthetically standardized draw.io diagrams across major cloud platforms and BPMN, with declarative layout, stencils, and validation.

620
Stars
84/100
信任
分类: design-creative审计

给纯文本 LLM agent 装上眼睛:图片问答、OCR、截图分析、视觉定位等一套视觉工具箱 + skill,并可无缝接入 Codex、Claude Code、OpenCode、Pi | Give text-only LLM agents vision: image Q&A, OCR, screenshot understanding, visual grounding, image-to-SVG - a vision toolkit & skill, with drop-in integration for Codex, Claude Code, OpenCode, Pi

321
Stars
77/100
信任
分类: utility审计

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

2.1K
Stars
85/100
信任
分类: ml-automation审计

Ground truth layer for humans and AI agents working together. Version control for knowledge.

1.4K
Stars
84/100
信任
分类: agent-frameworks审计

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
信任
分类: data-analysis审计

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
信任
分类: research审计

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

6.2K
Stars
75/100
信任
分类: ml-automation审计