By wshobson
Build production Apache Airflow DAGs following best practices for operators, sensors, testing, and deployment, enabling you to create data pipelines and orchestrate workflows with reliable batch job scheduling.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub p/wshobson-wshobson-airflow-dag-patterns-plugins-data-engineering-skills-airflow-dag-patternsDependency auditing, version management, and security vulnerability scanning
SAST analysis, dependency vulnerability scanning, OWASP Top 10 compliance, container security scanning, and automated security hardening
Database architecture, schema design, and SQL optimization for production systems
Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration
Orchestrate complex workflows with DAG-based execution, parallel tasks, and run history tracking
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
Pi Flow — author, enhance, and run structured filesystem-coordinated workflows (a DAG of producer/verify nodes) as a fleet of efficient pi agents driven by non-Claude coding-plan models, with Claude Code as the single console. Three skills: piflow-init (create a workflow), piflow-enhance (improve it), piflow-start (run + monitor it).
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Editorial "Data Engineering" bundle for Claude Code from Antigravity Awesome Skills.