MCP App StoreMCP App Store
from claude-skills209
Provides senior MLOps guidance: build and validate ML pipelines, deploy and monitor models in production, set up feature stores, and automate CI/CD for models.
This skill equips an agent to act as a senior MLOps engineer: assess ML maturity, design and validate end-to-end training pipelines, deploy models for real-time or batch serving, register and promote models, and implement monitoring and drift detection. It includes runnable examples (FastAPI serving, Kubernetes manifests), drift-detection code, Feast feature-store patterns, and scripts for model registry operations.
Invoke this skill when you need production-ready guidance for moving ML work from experiments to reliable services: choosing serving topology, writing health checks and autoscaling policies, gating deployments with evaluation thresholds, instrumenting P50/P95/P99 SLOs, or configuring feature stores and online retrieval. Useful during deployment planning, incident triage, and pipeline design reviews.
Well-suited for coding-capable agents that can reason about infrastructure and Python examples (Claude Code, Codex, Cursor, Gemini CLI). The skill expects the agent to produce code snippets, YAML manifests, and operational guidance.
Well-structured MLOps skill with three utility scripts for drift detection, model registry management, and pipeline validation. SKILL.md provides comprehensive workflow guidance with K8s deployment examples, monitoring patterns, and feature store integration. Scripts use only standard library, no external deps. Scripts exited with usage messages under DRY_RUN since they require file arguments, but the code is clean and functional.
Solid skill with no security concerns. All scripts are self-contained Python using only stdlib. Good documentation with clear CLI interfaces. Scripts don't support a DRY_RUN mode so they show usage when called without args, which is expected behavior.
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