mlops-coding-skills

In the author's own words, from each skill's SKILL.md.

• mlops-automation

  Automate an MLOps project with mise tasks, lefthook hooks, Docker images, GitHub Actions, and MLflow tracking on a SQL backend. Use when adding a task runner, git hooks, CI/CD, or experiment tracking to a working package.

  mlops-automation/SKILL.md on GitHub (opens in a new tab)

• mlops-collaboration

  Prepare a project for public collaboration — license, code of conduct, docs, branch rulesets, templates, and git-cliff releases. Use when open-sourcing a repository, onboarding contributors, or cutting a tagged release.

  mlops-collaboration/SKILL.md on GitHub (opens in a new tab)

• mlops-industrialization

  Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.

  mlops-industrialization/SKILL.md on GitHub (opens in a new tab)

• mlops-initialization

  Initialize a production-ready Python MLOps project with uv, git, mise, and a shared editor setup. Use when starting a new repository, writing its first pyproject.toml, or repairing an inconsistent project skeleton.

  mlops-initialization/SKILL.md on GitHub (opens in a new tab)

• mlops-observability

  Make an ML system a glass box with reproducible runs, MLflow dataset lineage, drift monitoring, alerting, and SHAP explanations. Use when a deployed model needs traceability, monitoring, alerting, or explanation.

  mlops-observability/SKILL.md on GitHub (opens in a new tab)

• mlops-prototyping

  Structure reproducible Jupyter notebooks with a fixed section layout, hoisted configuration, and leakage-free scikit-learn pipelines. Use when exploring a dataset, training a first model, or preparing a notebook for promotion.

  mlops-prototyping/SKILL.md on GitHub (opens in a new tab)

• mlops-validation

  Add the validation layers that gate a merge — ty typing, Ruff linting, pytest coverage, structured logging, and the trivy, pip-audit, and gitleaks scans. Use when hardening code quality or wiring the mise run check task.

  mlops-validation/SKILL.md on GitHub (opens in a new tab)

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