Data Engineering

This skill should be used when the user asks about "Unity editor scripting", "Custom Inspector", "EditorWindow", "PropertyDrawer", "Unity Input System", "new Input System", "UI Toolkit", "uGUI", "Canvas", "asset management", "AssetDatabase", "build pipeline", "Editor utilities", or needs guidance on Unity editor extensions, input handling, UI systems, and workflow optimization.

Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups. Includes CAC calculator, channel playbooks, HubSpot integration, and international expansion tactics. Use when planning demand generation campaigns, optimizing paid media, building SEO strategies, establishing partnerships, or when user mentions demand gen, paid ads, LinkedIn ads, Google ads, CAC, acquisition, lead generation, or pipeline generation.

Understand and adhere to the project's technology stack including Laravel, PHP, React, PostgreSQL, Pest, Tailwind CSS, and all configured tools and services. Use this skill when making architectural decisions, when choosing libraries or packages, when configuring development tools, when setting up testing frameworks, when implementing authentication, when integrating third-party services, when configuring CI/CD pipelines, when setting up local development environments, or when ensuring consistency with the established tech stack across all parts of the application.

Use when working on Acuantia's BigQuery Dataform pipeline (acuantia-gcp-dataform project) - adds Acuantia-specific patterns on top of dataform-engineering-fundamentals: ODS two-arg ref() syntax, looker_ filename prefix, Looker integration (looker_prod/looker_dev), acuantia dataset conventions, coordination with callrail_data_export/dialpad_data_integration/looker projects

Process data files through transformation pipelines with validation, cleaning, and export. Use for CSV/Excel/JSON data processing, encoding handling, batch operations, and data transformation workflows.

Data engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems. ML algorithms, deep learning, and AI.

Guide for implementing Google Cloud SDK (gcloud CLI) - a command-line tool for managing Google Cloud resources. Use when installing/configuring gcloud, authenticating with Google Cloud, managing projects/configurations, deploying applications, working with Compute Engine/GKE/App Engine/Cloud Storage, scripting gcloud operations, implementing CI/CD pipelines, or troubleshooting Google Cloud deployments.

BigQuery Object Tables를 활용한 비정형 데이터(오디오, 이미지 등) 분석 및 Audio Analytics Agent 구축 가이드. GCS 데이터 연동, 메타데이터 캐싱, AI 모델 통합, ADK 에이전트 구현 패턴을 다룹니다.

PySpark fundamentals for distributed data processing.

Sets up a CI/CD pipeline for Python projects using GitHub Actions. Includes templates for PR checks, deployment via SSH, pre-commit hooks, and email notifications. Use this skill when a user wants to create a new CI/CD pipeline for a Python project.

Schedule a delayed or interval-based action with minimal heartbeats. Use to wait before running a check (e.g., GH Actions, PR status, CI pipeline) or to poll on a bounded interval. Trigger on: "wait", "delay", "poll", "check later", "retry after", "schedule check".

Automated CI/CD pipeline setup using GitHub Actions with automatic secret configuration via GitHub CLI. Generates platform-specific workflows (Vercel, DigitalOcean, Railway) and configures repository secrets automatically. Use when setting up continuous deployment, configuring GitHub Actions, automating deployments, or when user mentions CI/CD, GitHub Actions, automated deployment, or pipeline setup.

Load when working on data pipelines, datasets, reproducibility, or datainfrastructure topics. Contains best practices for data engineering,ETL/ELT patterns, and ensuring reproducible data workflows.

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

Data engineering, ETL pipelines, data warehousing, and analytics. Master data infrastructure and pipeline design.

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

Comprehensive Python data engineering patterns for AWS Data Lake, including PySpark, Pandas, Apache Airflow, AWS Glue, ETL pipelines, data quality, schema management, performance optimization, FastAPI services, streaming with Kafka/Kinesis, data validation with Great Expectations, testing strategies, error handling, logging, and production deployment on AWS EMR and Glue.

Central coordinator for blog writing workflow with multi-agent execution. USE WHEN user says 'write a blog post', 'create blog content', 'start blog workflow', OR user wants to orchestrate the full blog writing pipeline.

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.