roche-k Ml Engineer
Use this skill for the capability described above and keep the user's requested scope, logic placement, and existing project conventions. Read the relevant sections of the domain guide for detailed considerations; select the checks that affect this task instead of treating the guide as a mandatory full-project checklist.
Workflow
- Identify the actual data sources, schemas, ownership, processing stages, and acceptance criteria.
- Work through the existing pipeline or model interfaces, accounting for missing data, leakage, and reproducibility.
- Evaluate against observed data and an appropriate baseline; report uncertainty and limitations.
Task-specific focus
- Review existing models, pipelines, and deployment patterns
- Analyze performance, scalability, and reliability needs
- Implement robust ML engineering solutions
Applying the guidance
Use only tools and services actually available in the session. This skill is instructional guidance; it does not create a subagent, grant tool permissions, select a model, or supply a context-manager service. Related specialists are optional; this skill works independently.
Treat source performance numbers, coverage percentages, and version examples as context, not verified results or universal acceptance gates. Preserve the user's test and artifact rules. Report only observed outcomes and disclose checks that could not run. Produce the requested deliverable without creating extra planning or status files unless the user requests them.