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mlops-collaboration
Guide to prepare MLOps projects for sharing, collaboration, and community engagement.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Guide to prepare MLOps projects for sharing, collaboration, and community engagement.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
| name | mlops-collaboration |
| description | Guide to prepare MLOps projects for sharing, collaboration, and community engagement. |
To transform a private project into a public, collaborative resource by establishing Governance (License, Code of Conduct), Documentation (README, Contributing), Standardization (Templates, Workstations), and Release Management.
Set the rules of engagement.
CODE_OF_CONDUCT.md to foster a safe community.main branch (require PRs, status checks)..gemini/config.yaml)..gitignore (exclude secrets, data, venvs).Make the project usable and understandable.
README.md gets too long.git-cliff (replaces Commitizen); commit the rendered file.Eliminate "it works on my machine".
cookiecutter for scaffolding and cruft update to keep projects synced..devcontainer/devcontainer.json.
Ship with confidence.
git-cliff from the commit history (replaces Commitizen/Keep-a-Changelog by hand).main is stable).develop + release branches).git-cliff changelog -> annotated vX.Y.Z tag (git tag -a vX.Y.Z) -> gh release create vX.Y.Z.LICENSE file present?README.md have installation instructions?CONTRIBUTING.md clear?.devcontainer/devcontainer.json exist?vX.Y.Z) via gh release create?CHANGELOG.md generated by git-cliff from Conventional Commits?Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.
Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.
Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.
Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
基于 SOC 职业分类