mlops-collaboration: Coding Workflows for AI Agents | OpenAgentSkill

Overview

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.

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MLOps Collaboration

Goal

To transform a private project into a public, collaborative resource by establishing Governance (License, Code of Conduct, branch rulesets), Documentation (README, AGENTS, Contributing), Standardization (Templates, Workstations), and Release Management.

Prerequisites

Language: Python 3.14
Platform: GitHub
Context: Open sourcing or team collaboration.

Instructions

1. Repository Governance

Set the rules of engagement.

License: Pick an SPDX identifier and commit the full text. Declare it in pyproject.toml as license = "MIT" plus license-files = ["LICENSE.txt"] (PEP 639), and make sure the file itself carries its title and copyright line — a bare license body with no Copyright (c) <year> <author> is legally ambiguous.
Code of Conduct: Add CODE_OF_CONDUCT.md to foster a safe community.
Branch Protection (concretely): commit .github/rulesets/main.json and apply it with mise run install:rulesets. A ruleset in the repository is reviewable, diffable, and restorable; a setting clicked in the web UI is none of those. A useful baseline blocks deletion and non-fast-forward pushes, requires linear history, requires a pull request, and requires the CI status check to pass.
- Make the task idempotent: look the ruleset up by name and PUT over it when it exists, POST only when it does not. A plain POST creates a duplicate ruleset on every run.
- The required status check must name the CI job id exactly. If you rename the job, the ruleset waits forever for a context that no longer reports.
Review: Automate preliminary reviews with tools like Gemini Code Assist (.gemini/config.yaml).
Ignore: Comprehensive .gitignore (exclude secrets, data, virtualenvs, and local MLflow state such as mlflow.db and mlartifacts/).

2. Comprehensive Documentation

Make the project usable and understandable.

README.md: The landing page for humans (Badges, Hook, Quickstart, commands).
AGENTS.md: The landing page for AI assistants — project overview, setup and core commands, definition of done, conventions and idioms, repository layout, in that order. Keep both files in sync with reality; when a command changes, both change in the same commit.
Describe the real stack: state the versions a newcomer will actually install — Python 3.14, MLflow 3.15 on a SQL tracking store (sqlite:///mlflow.db locally, not the deprecated file store), Ruff 0.16, ty 0.0.69, uv, mise. A README that documents last year's stack costs more time than no README.
MkDocs: Use for full documentation sites (API ref, tutorials) when README.md gets too long.
CONTRIBUTING.md: Guide for developers — environment setup, branch naming, PR process, and the exact local gate (mise run all) they must pass before opening a pull request.
CHANGELOG.md: Generate from Conventional Commits with git-cliff (replaces Commitizen); commit the rendered file.

3. Standardization & Workstations

Eliminate "it works on my machine".

Templates: Use cookiecutter for scaffolding and cruft update to keep projects synced.
Baseline (required): mise pins the toolchain (mise.lock) and uv pins the Python dependencies (uv.lock). Together they are what actually makes two machines identical, and they work with any editor.
Devcontainer (recommended, not required): .devcontainer/devcontainer.json adds one-click GitHub Codespaces and a pinned OS-level image. It is a genuine improvement for teams onboarding non-Python contributors, but it is not part of the course's reference package or its cookiecutter template today — so treat it as an upgrade to propose, not a box a project must tick. If you add one, install mise in the image and let it install everything else, so the devcontainer and a bare laptop resolve the same versions.

4. Release Management

Ship with confidence.

Versioning: Follow SemVer (MAJOR.MINOR.PATCH) driven by Conventional Commits.
Changelog: Generate with git-cliff from the commit history (replaces Commitizen/Keep-a-Changelog by hand).
Workflows:
- GitHub Flow: Small teams, continuous delivery (main is stable).
- Git Flow: Scheduled releases (develop + release branches).
- Forking: Open source, distributed contributors.
Process: mise run all green -> bump version -> git-cliff changelog -> annotated vX.Y.Z tag (git tag -a vX.Y.Z) -> gh release create vX.Y.Z.

Self-Correction Checklist

License: Is a LICENSE file present, titled, copyrighted, and declared with SPDX + license-files?
Readme: Does README.md have installation instructions that match the real commands?
Agents: Does AGENTS.md exist and describe the current stack and gate?
Contributing: Does CONTRIBUTING.md state the branch convention and require mise run all?
Protection: Is .github/rulesets/main.json committed and applied idempotently by mise run install:rulesets?
Reproducibility: Are uv.lock and mise.lock committed? (A devcontainer is a recommended bonus, not a requirement.)
SemVer: Are releases semver-tagged (vX.Y.Z) via gh release create?
Changelog: Is CHANGELOG.md generated by git-cliff from Conventional Commits?

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File metadata

name: mlops-collaboration
description: 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.
license: MIT
metadata:
  author: Médéric HURIER (Fmind)
  source: github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-collaboration
  created: 2026-01-25
  updated: 2026-08-10

View original text

---
name: mlops-collaboration
description: 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.
license: MIT
metadata:
  author: Médéric HURIER (Fmind)
  source: github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-collaboration
  created: 2026-01-25
  updated: 2026-08-10
---

# MLOps Collaboration

## Goal

To transform a private project into a public, collaborative resource by establishing **Governance** (License, Code of Conduct, branch rulesets), **Documentation** (README, AGENTS, Contributing), **Standardization** (Templates, Workstations), and **Release Management**.

## Prerequisites

- **Language**: Python 3.14
- **Platform**: GitHub
- **Context**: Open sourcing or team collaboration.

## Instructions

### 1. Repository Governance

Set the rules of engagement.

1. **License**: Pick an SPDX identifier and commit the full text. Declare it in `pyproject.toml` as `license = "MIT"` plus `license-files = ["LICENSE.txt"]` (PEP 639), and make sure the file itself carries its title and copyright line — a bare license body with no `Copyright (c) <year> <author>` is legally ambiguous.
1. **Code of Conduct**: Add `CODE_OF_CONDUCT.md` to foster a safe community.
1. **Branch Protection (concretely)**: commit `.github/rulesets/main.json` and apply it with `mise run install:rulesets`. A ruleset in the repository is reviewable, diffable, and restorable; a setting clicked in the web UI is none of those. A useful baseline blocks deletion and non-fast-forward pushes, requires linear history, requires a pull request, and requires the CI status check to pass.
   - Make the task **idempotent**: look the ruleset up by name and `PUT` over it when it exists, `POST` only when it does not. A plain `POST` creates a duplicate ruleset on every run.
   - The required status check must name the CI **job id** exactly. If you rename the job, the ruleset waits forever for a context that no longer reports.
1. **Review**: Automate preliminary reviews with tools like **Gemini Code Assist** (`.gemini/config.yaml`).
1. **Ignore**: Comprehensive `.gitignore` (exclude secrets, data, virtualenvs, and local MLflow state such as `mlflow.db` and `mlartifacts/`).

### 2. Comprehensive Documentation

Make the project usable and understandable.

1. **README.md**: The landing page for humans (Badges, Hook, Quickstart, commands).
1. **AGENTS.md**: The landing page for AI assistants — project overview, setup and core commands, definition of done, conventions and idioms, repository layout, in that order. Keep both files in sync with reality; when a command changes, both change in the same commit.
1. **Describe the real stack**: state the versions a newcomer will actually install — Python 3.14, MLflow 3.15 on a SQL tracking store (`sqlite:///mlflow.db` locally, not the deprecated file store), Ruff 0.16, `ty` 0.0.69, `uv`, `mise`. A README that documents last year's stack costs more time than no README.
1. **MkDocs**: Use for full documentation sites (API ref, tutorials) when `README.md` gets too long.
1. **CONTRIBUTING.md**: Guide for developers — environment setup, branch naming, PR process, and the exact local gate (`mise run all`) they must pass before opening a pull request.
1. **CHANGELOG.md**: Generate from **Conventional Commits** with `git-cliff` (replaces Commitizen); commit the rendered file.

### 3. Standardization & Workstations

Eliminate "it works on my machine".

1. **Templates**: Use `cookiecutter` for scaffolding and `cruft update` to keep projects synced.
1. **Baseline (required)**: `mise` pins the toolchain (`mise.lock`) and `uv` pins the Python dependencies (`uv.lock`). Together they are what actually makes two machines identical, and they work with any editor.
1. **Devcontainer (recommended, not required)**: `.devcontainer/devcontainer.json` adds one-click GitHub Codespaces and a pinned OS-level image. It is a genuine improvement for teams onboarding non-Python contributors, but it is **not** part of the course's reference package or its cookiecutter template today — so treat it as an upgrade to propose, not a box a project must tick. If you add one, install `mise` in the image and let it install everything else, so the devcontainer and a bare laptop resolve the same versions.

### 4. Release Management

Ship with confidence.

1. **Versioning**: Follow **SemVer** (MAJOR.MINOR.PATCH) driven by **Conventional Commits**.
1. **Changelog**: Generate with `git-cliff` from the commit history (replaces Commitizen/Keep-a-Changelog by hand).
1. **Workflows**:
   - **GitHub Flow**: Small teams, continuous delivery (`main` is stable).
   - **Git Flow**: Scheduled releases (`develop` + `release` branches).
   - **Forking**: Open source, distributed contributors.
1. **Process**: `mise run all` green -> bump version -> `git-cliff` changelog -> annotated `vX.Y.Z` tag (`git tag -a vX.Y.Z`) -> `gh release create vX.Y.Z`.

## Self-Correction Checklist

- [ ] **License**: Is a `LICENSE` file present, titled, copyrighted, and declared with SPDX + `license-files`?
- [ ] **Readme**: Does `README.md` have installation instructions that match the real commands?
- [ ] **Agents**: Does `AGENTS.md` exist and describe the current stack and gate?
- [ ] **Contributing**: Does `CONTRIBUTING.md` state the branch convention and require `mise run all`?
- [ ] **Protection**: Is `.github/rulesets/main.json` committed and applied idempotently by `mise run install:rulesets`?
- [ ] **Reproducibility**: Are `uv.lock` and `mise.lock` committed? (A devcontainer is a recommended bonus, not a requirement.)
- [ ] **SemVer**: Are releases semver-tagged (`vX.Y.Z`) via `gh release create`?
- [ ] **Changelog**: Is `CHANGELOG.md` generated by `git-cliff` from Conventional Commits?

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: credential or environment access, network or browser surface
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: MLOps-Courses/mlops-coding-skills
License: MIT
Version: Unknown
Last GitHub push: Aug 10, 2026
Registry updated: Sep 13, 2026
Instruction path: mlops-collaboration/SKILL.md @ 4a146e6c4d47

Version reported in registry metadata; check source releases before relying on it.

Quality

49/100

Needs review

Trust

57/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Creator: Médéric HURIER (Fmind)
Source: MLOps-Courses/mlops-coding-skills
Indexed by: OpenAgentSkill community index

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