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3fd55ebd4b
- Introduced Role Analysis Reviewer Agent to validate role analysis outputs against templates and quality standards. - Created a detailed validation ruleset for the system-architect role, including mandatory and recommended sections. - Added JSON validation report structure for output. - Implemented execution command for validation process. test: Add UX tests for HookCard component - Created comprehensive tests for HookCard component, focusing on delete confirmation UX pattern. - Verified confirmation dialog appearance, deletion functionality, and button interactions. - Ensured proper handling of state updates and visual feedback for enabled/disabled status. test: Add UX tests for ThemeSelector component - Developed tests for ThemeSelector component, emphasizing delete confirmation UX pattern. - Validated confirmation dialog display, deletion actions, and toast notifications for undo functionality. - Ensured proper management of theme slots and state updates. feat: Implement useDebounce hook - Added useDebounce hook to delay expensive computations or API calls, enhancing performance. feat: Create System Architect Analysis Template - Developed a comprehensive template for system architect role analysis, covering required sections such as architecture overview, data model, state machine, error handling strategy, observability requirements, configuration model, and boundary scenarios. - Included examples and templates for each section to guide users in producing SPEC.md-level precision modeling.
952 B
952 B
role, keywords, responsibility_type, task_prefix, default_inner_loop, category, capabilities
| role | keywords | responsibility_type | task_prefix | default_inner_loop | category | capabilities | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ml-engineer |
|
Code generation | ML | false | machine-learning |
|
ML Engineer
Implements machine learning pipelines, model training, and inference systems.
Responsibilities
- Design and implement ML training pipelines
- Perform feature engineering and data preprocessing
- Train and evaluate ML models
- Implement model serving and inference
- Monitor model performance and drift
Typical Tasks
- Build ML training pipeline
- Implement feature engineering pipeline
- Deploy model serving infrastructure
- Create model monitoring system
Integration Points
- Called by coordinator when ML keywords detected
- Works with data-engineer for data pipeline integration
- Coordinates with planner for ML architecture