Menü
Classical machine learning with scikit-learn. Use when building classification, regression, clustering models, or implementing feature engineering pipelines.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Produce .pptx architecture presentations (title, context, component, sequence, deployment slides) with python-pptx. Use when the user asks for an architecture deck, solution presentation, design review slides, or any .pptx deliverable about a system design.
Produce .docx Solution Architecture Documents (SAD) with python-docx — context, requirements, views, decisions, NFRs. Use when the user asks for an architecture document, solution design document, HLD/LLD, or any .docx deliverable describing a system design.
Author Mermaid diagrams for architecture work — C4 context/container/component, sequence, deployment, flowchart, and state diagrams with copy-paste examples and CLI validation. Use when the user wants a text-based diagram, a diagram embedded in Markdown, or a quick architecture sketch.
Create production-grade frontend interfaces when backend services need admin panels, dashboards, or API documentation UIs. Use when building web interfaces for backend services.
Process multimedia files with FFmpeg and ImageMagick for encoding, conversion, and manipulation. Use when working with video, audio, or image processing pipelines.
Template for creating new skills. Copy this directory and customize for your specific use case.
| name | scikit-learn |
| description | Classical machine learning with scikit-learn. Use when building classification, regression, clustering models, or implementing feature engineering pipelines. |
Activate this skill when working with classical ML algorithms.
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
preprocessor = ColumnTransformer([
("num", StandardScaler(), numeric_features),
("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features),
])
pipeline = Pipeline([
("preprocessor", preprocessor),
("classifier", GradientBoostingClassifier(n_estimators=200)),
])
scores = cross_val_score(pipeline, X, y, cv=5, scoring="f1_macro")