Build Apache Airflow data pipelines with clear task dependencies and schedules. This skill guides workflow design, retries, monitoring, local testing, and production deployment, with patterns for investigating failed runs.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Review the instructions the agent will receive before you install.
Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.
Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.
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Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.
Review code and infrastructure for security risks, map potential threats, and plan stronger defenses. The skill covers OWASP checks, attack and defense exercises, and incident response, with validation required for each environment.
Turn a repeatable task into an Andru.ia skill with clear inputs, outputs, examples, and limits. Draft SKILL.md, check the folder structure and required validations, and prepare a pull request describing the changes for review.
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Plan a 360-degree or peer feedback process and create empty response templates from confirmed requirements. Produces consistent CSV, SQL, JSON Schema, or Notion property mappings while keeping visibility choices and scoring configuration separate.