ml-engineer

ml-engineer is an MLOps & model serving skill for AI agents, published by OpenCoven in the coven repository on GitHub. Skillspace rates ml-engineer C (74/100) and it passed the safety scan; the rating is based on static analysis only.

Build production ML systems: model training pipelines, serving infrastructure, performance optimization, and automated retraining. Use when: (1) designing or building ML pipelines (data validation → training → deployment), (2) optimizing model training (hyperparameter search, distributed training, checkpointing), (3) deploying models to production (REST/gRPC endpoints, batch/stream processing, canary releases), (4) setting up ML monitoring (prediction drift, feature drift, performance decay), (5) implementing feature engineering or feature stores, (6) automating retraining triggers, (7) debugging model performance or serving latency issues. Triggers on: ML pipeline, model training, model serving, feature engineering, hyperparameter tuning, model deployment, inference optimization, model monitoring, MLOps, retraining.

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

34

Unproven: rated by static analysis only, not yet compared head-to-head.

Not launch-quality. It is graded C with a clean safety verdict, but our quality filters exclude it: it is a near-duplicate variant of another skill. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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