machine-learning-ops-ml-pipeline
Design and implement a complete ML pipeline for: $ARGUMENTS
Profile →npx skills add sickn33/agentic-awesome-skills --skill machine-learning-ops-ml-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Machine Learning Pipeline - Multi-Agent MLOps Orchestration Design and implement a complete ML pipeline for: $ARGUMENTS ## Use this skill when - Working on machine learning pipeline - multi-agent mlops orchestration tasks or workflows - Needing guidance, best practices, or checklists for machine learning pipeline - multi-agent mlops orchestration ## Do not use this skill when - The task is unrelated to machine learning pipeline - multi-agent mlops orchestration - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Thinking This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes: - **Phase-based coordination**: Each phase builds upon previous outputs, with clear handoffs between agents - **Modern tooling integration**: MLflow/W&B for experiments, Feast/Tecton for features, KServe/Seldon for serving - **Production-first mindset**:
- Use this skill when
- Do not use this skill when
- Instructions
- Thinking
- Phase 1: Data & Requirements Analysis
- Phase 2: Model Development & Training
- Phase 3: Production Deployment & Serving
- Phase 4: Monitoring & Continuous Improvement
- Configuration Options
- Success Criteria
- Final Deliverables
- Limitations
What does the machine-learning-ops-ml-pipeline skill do?
Design and implement a complete ML pipeline for: $ARGUMENTS
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill machine-learning-ops-ml-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.