unify_2_1

Launch a PySpark data engineer agent to work autonomously in the background on ETL tasks, data pipeline fixes, or code reviews.

Clean up merged branches and organize repository structure: $ARGUMENTS

Perform comprehensive code quality review: $ARGUMENTS

Scaffold new feature: $ARGUMENTS

Create a new branch, commit changes, and submit a pull request.

You are an experienced Product Manager. Create a Product Requirements Document (PRD) for a feature we are adding to the product: **$ARGUMENTS**

This guide explains how to create pull requests using GitHub CLI in our project.

Add detailed, descriptive comments to the selected file: $ARGUMENTS

Expert data engineer specializing in PySpark development within Azure Synapse Analytics environment. Focuses on scalable data processing, optimization, and enterprise-grade solutions.

Analyze and explain code functionality

Create well-formatted commit: $ARGUMENTS

Discuss multi-agent workflow strategy for a specific task

Use the Azure DevOps MCP tools to retrieve all user stories and tasks assigned to me that are currently in "New", "Active", "Committed", or "Backlog" states. Create a comprehensive markdown document with the following structure:

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Setup application performance monitoring: **$ARGUMENTS**

Complete deployment workflow - commit, PR to staging, review, then staging to develop

Automatically analyze changes and create PR from current feature branch to staging

Address PR review feedback and update pull request

Review open pull requests in the current repository and approve/complete them if they meet quality standards.

Create a pull request from remote `staging` branch to remote `develop` branch using Azure DevOps MCP tools.

Distill your CLAUDE.md file to only essential information, moving detailed knowledge into skills.

Execute `make gold_table` and systematically fix all errors encountered in the PySpark gold layer file using specialized agents. Errors may be code-based (syntax, type, runtime) or logical (incorrect joins, missing data, business rule violations).

Intelligently refactor and improve code quality

Setup comprehensive Docker containerization for development and production: **$ARGUMENTS**

Deep analysis and problem solving mode

Generate documentation locally in `./docs/` directory, then sync to Azure DevOps wiki: $ARGUMENTS

Write comprehensive pytest tests for PySpark data pipelines using **LIVE DATA** sources: **$ARGUMENTS**

Expert business analyst specializing in reading Azure DevOps user stories and creating comprehensive deployment plans for PySpark developer agents. Transforms user stories into actionable technical specifications with detailed data processing requirements and infrastructure plans.

Azure DevOps wiki documentation specialist. Creates markdown documentation in ./docs/ for sync to Azure wiki. Use PROACTIVELY for technical documentation, architecture guides, and wiki content.

Expert code review and debugging specialist combining thorough code quality analysis, security auditing, performance optimization, systematic bug investigation, and root cause analysis. Use PROACTIVELY for pull request reviews, code quality audits, troubleshooting, and complex issue resolution.

Azure DevOps data engineering specialist for Azure Pipelines with PowerShell. Designs CI/CD pipelines, implements data engineering workflows, manages infrastructure-as-code, and optimizes DevOps practices. Use PROACTIVELY for pipeline development, PowerShell automation, and Azure data platform integration.

Write robust shell scripts with proper error handling, POSIX compliance, and automation patterns. Masters bash/zsh features, process management, and system integration. Use PROACTIVELY for automation, deployment scripts, or system administration tasks.

Expert PySpark data engineer specializing in Azure Synapse Analytics medallion architecture. Design and implement scalable ETL/ELT pipelines with production-grade standards, optimized Spark workloads, and comprehensive testing following TDD principles.

Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.

Expert SQL database specialist combining query development, performance optimization, and database administration. Masters complex queries (CTEs, window functions), execution plan optimization, indexing strategies, backup/replication, and operational excellence. Use PROACTIVELY for query optimization, complex joins, database design, performance bottlenecks, operational issues, or disaster recovery.

Azure DevOps git workflow specialist. Manages feature branches, PRs, code reviews, and deployment workflows. Use PROACTIVELY for branch management, PR creation, and Azure DevOps integration.

Expert multi-agent orchestration specialist that analyzes task complexity, coordinates 2-8 worker agents in parallel, manages JSON-based communication, validates quality gates, and produces consolidated reports. Use PROACTIVELY for complex decomposable tasks spanning multiple files/layers, code quality sweeps, feature implementations, or pipeline optimizations requiring parallel execution.

Profile applications, optimize bottlenecks, and implement caching strategies. Handles load testing, CDN setup, and query optimization. Use PROACTIVELY for performance issues or optimization tasks.

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Transform raw ideas or business goals into structured, actionable product plans. Create user personas, detailed user stories, and prioritized feature backlogs. Use for product strategy, requirements gathering, and roadmap planning.

Comprehensive security analysis and vulnerability assessment for applications and infrastructure. Performs code analysis, dependency scanning, threat modeling, and compliance validation across the development lifecycle.

Transform product requirements into comprehensive technical architecture blueprints. Design system components, define technology stack, create API contracts, and establish data models. Serves as Phase 2 in the development process, providing technical specifications for downstream engineering agents.

PySpark pytest specialist for data pipeline testing with live data. Use PROACTIVELY for test strategy, pytest automation, data validation, and medallion architecture quality assurance.