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Found 7 Skills
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
Agent skill for data-ml-model - invoke with $agent-data-ml-model
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
Designs and implements CI/CD pipelines for automated testing, building, deployment, and security scanning across multiple platforms. Covers pipeline optimization, test integration, artifact management, and release automation.
Author ZenML pipelines: @step/@pipeline decorators, type hints, multi-output steps, dynamic vs static pipelines, artifact data flow, ExternalArtifact, YAML configuration, DockerSettings for remote execution, custom materializers, metadata logging, secrets management, and custom visualizations. Use this skill whenever asked to write a ZenML pipeline, create ZenML steps, make a pipeline work on Kubernetes/Vertex/SageMaker, add Docker settings, write a materializer, create a custom visualization, handle "works locally but fails on cloud" issues, or configure pipeline YAML files. Even if the user doesn't explicitly mention "pipeline authoring", use this skill when they ask to build an ML workflow, data pipeline, or training pipeline with ZenML.
Agent skill for neural-network - invoke with $agent-neural-network