Provides a pipeline-based framework for end-to-end machine learning, including data processing and deployment.
Install
mkdir -p .claude/skills/skills-zhulei227 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18533" && unzip -o skill.zip -d .claude/skills/skills-zhulei227 && rm skill.zipInstalls to .claude/skills/skills-zhulei227
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
EasyMLOps 是一个基于 Pipeline 模式构建的机器学习建模任务框架，提供从数据处理、特征工程、模型训练到生产部署的完整流程支持。Key capabilities
- →Process tabular data with cleaning and feature engineering
- →Handle NLP tasks including text cleaning and classification
- →Manage time series data with specialized processing and DNN models
- →Perform vision tasks like object detection and pose estimation
How it works
The skill provides a framework for machine learning tasks based on a Pipeline pattern, offering modules for data processing, feature engineering, model training, and deployment across various data types and task categories.
Inputs & outputs
When to use skills
- →Processing tabular data
- →Building NLP pipelines
- →Deploying machine learning models
About this skill
EasyMLOps 机器学习 Pipeline 框架
EasyMLOps 是一个基于 Pipeline 模式构建的机器学习建模任务框架，提供从数据处理、特征工程、模型训练到生产部署的完整流程支持。
核心模块导入
from easymlops import TablePipeLine, NLPPipeline, TSPipeLine
from easymlops.yolo import YOLOPipeline
from easymlops.ocr import OCRPipeLine
主要功能模块
| 模块 | 说明 |
|---|---|
| TablePipeLine | 表格数据处理：数据清洗、特征编码、特征降维、特征选择、因子分解机(FM/FFM/DeepFM)、分类回归、Stacking |
| NLPPipeline | NLP任务：文本清洗、分词、特征提取、文本分类、相似度检索 |
| TSPipeLine | 时序任务：时间序列处理、DNN模型 |
| YOLOPipeline | 视觉任务：目标检测、实例分割、图像分类、姿态估计、旋转目标检测 |
| OCRPipeLine | OCR任务：文本检测与识别，支持80+语言 |
| AutoML | 自动化机器学习 |
文档目录
When not to use it
- →When the task is outside the scope of machine learning modeling
- →When the specific modules provided do not cover the required functionality
Limitations
- →Based on a Pipeline pattern for machine learning tasks
- →Modules are specialized for specific data types and tasks
- →Supports a defined set of functionalities for each module
How it compares
This skill offers specialized, pre-built pipelines for common machine learning tasks, providing a structured and efficient workflow compared to building each component and pipeline manually.
Compared to similar skills
skills side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| skills (this skill) | 0 | 3mo | No flags | Intermediate |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| umap-learn | 6 | 1mo | Review | Intermediate |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
You might also like
quant-analyst
zenobi-us
Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
umap-learn
K-Dense-AI
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
embedding-strategies
wshobson
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
building-automl-pipelines
jeremylongshore
Build automated machine learning pipelines, including feature engineering, model selection, and performance evaluation.
model-compare
rawwerks
Compare 3D CAD models using boolean operations (IoU, Dice, precision/recall). Use when evaluating generated models against gold references, diffing CAD revisions, or computing similarity metrics for ML training. Triggers on: model diff, compare models, IoU, intersection over union, model similarity, CAD comparison, STEP diff, 3D evaluation, gold reference, generated model, precision recall 3D.
matchms
davila7
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.