Validate Clean Architecture compliance including dependency flow verification (infra → interface → app → domain), module organization analysis, layer separation checks, and circular dependency detection. Use when reviewing code structure, analyzing imports, ensuring architectural principles, or validating refactoring changes.
Execute performance benchmarks for DataLoader configurations, training performance analysis, GPU utilization monitoring, and optimization validation. Use when analyzing performance bottlenecks, finding optimal training settings, validating speed improvements, or testing array bundling efficiency.
Validate that benchmark-training command stays in sync with learn-model command. Checks for missing CLI options, verifies test exclusion list accuracy, and identifies options that need to be added to benchmark-training when learn-model features are added. Use after adding new options to learn-model, before creating PRs that modify CLI commands, or when reviewing benchmark-training compatibility.
Execute cloud provider integration tests for Google Cloud Platform (GCP) and Amazon Web Services (AWS), validate S3 and GCS operations, test cloud storage authentication, verify bucket access, and debug cloud integration issues. Use when testing cloud storage features, validating parallel upload/download operations, or troubleshooting cloud connectivity.
Validate data pipeline configuration including array_type parameter verification, HCPE and preprocessing data format validation, storage configuration checks, and schema compliance verification. Use when configuring data sources, validating pipeline setup, debugging data loading issues, or ensuring data type consistency.
Manage Python dependencies using uv exclusively, add new packages, update existing dependencies, remove unused packages, and validate dependency compatibility. Use when adding packages, updating dependencies, resolving conflicts, or managing development dependencies. NEVER use pip directly.
Automate feature branch creation following project conventions, verify branch naming patterns, check base branch state, run initial QA validation, and prepare development environment. Use when starting new features, creating bug fix branches, or setting up development branches.
Create comprehensive GitHub pull requests using gh command with intelligently generated PR descriptions. Analyzes commit history, code changes, and project context to generate detailed PR bodies that include problem statement, solution approach, impact analysis, testing strategy, and reviewer guidance. Use when creating pull requests that require thorough documentation for effective code review.