salihcantekin Ml Pipeline Copilot Skill
Purpose
On-demand Copilot skill for data-and-ai/ml-pipeline. Use this when the user asks for focused help in this domain.
Invocation Triggers
- Requests to design, implement, refactor, review, optimize, or troubleshoot
Ml Pipelineconcerns. - Tasks requiring detailed production-grade practices rather than generic snippets.
Core Skill Focus
- robust architecture decisions
- production reliability
- security-first implementation
- test and observability completeness
Execution Procedure
- Clarify requirements and constraints.
- Propose minimal-risk technical approach.
- Implement with strong defaults for security, tests, and observability.
- Verify with explicit checks and summarize trade-offs.
Required Deliverables
- Concrete implementation guidance (not abstract recommendations only).
- Testing strategy (unit/integration/regression).
- Operational considerations (logging, metrics, rollout risk).
- Follow-up improvements for scalability and maintainability.
Modern Engineering Defaults
- Use typed interfaces/contracts where possible.
- Prefer incremental migrations over big-bang rewrites.
- Require compatibility and rollback notes for impactful changes.
- Ensure security and observability are not optional add-ons.
Acceptance Checklist
- Plan is technically sound and minimal-risk.
- Output is implementation-ready and not a placeholder.
- Validation steps are explicit and executable.
- Edge cases and failure paths are addressed.
- Trade-offs are clearly explained.
Do Not
- Produce placeholder outputs.
- Skip edge-case handling.
- Omit validation strategy.