workflow▌

sentry-workflow

End-to-end MLOps pipeline orchestration from data ingestion through model deployment and monitoring. \n \n Covers five core pipeline stages: data preparation, model training, validation, deployment, and monitoring with DAG orchestration patterns (Airflow, Dagster, Kubeflow) \n Includes data validation, feature engineering, experiment tracking integration, and model versioning strategies across the full ML lifecycle \n Provides deployment automation patterns including canary releases, blue-green

Declarative, Git-based continuous delivery for Kubernetes using ArgoCD or Flux CD. \n \n Supports both ArgoCD and Flux CD with installation, configuration, and repository structure guidance for each \n Covers core GitOps patterns including App of Apps, automated sync policies with pruning and self-healing, and progressive delivery strategies (canary, blue-green) \n Includes secret management approaches using External Secrets Operator and Sealed Secrets to keep credentials out of Git \n Provides

Guide for removing skills from an existing workflow and updating all related documentation.

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md，其中包含认证、权限处理

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md，其中包含认证、权限处理。然后阅读 ../lark-vc/SKILL.md，了解会议纪要相关操作。