mlops-initialization

A setup guide for starting a production-ready Python machine-learning operations project. MLOps means the tools and practices used to build, test, deploy, and maintain machine-learning systems.

In plain words
What is it for?
Use it when creating a new Python MLOps repository, its first pyproject.toml, or a broken project skeleton with uv, git, mise, and VS Code.
Why use it?
It creates a consistent project foundation with pinned tools, reproducible environments, version control, editor settings, and quality checks.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/mlops-courses/mlops-coding-skills/mlops-initialization
Any agent
npx skills add MLOps-Courses/mlops-coding-skills --skill mlops-initialization
Clone the repo
git clone --depth 1 https://github.com/MLOps-Courses/mlops-coding-skills

Made for: Claude Code, Codex.

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,776 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.02776
Opus 5 $0.00025 $0.01388
Sonnet 5 $0.00010 $0.00555
Haiku 4.5 $0.00005 $0.00278

Measured 2d ago against content hash 24f17b9b7010, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

mlops-initialization scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Downloads and executes remote codehighSupply chain

curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.

- If missing, install it: `curl -LsSf https://astral.sh/uv/install.sh | sh`

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- If missing, install it: `curl -LsSf https://astral.sh/uv/install.sh | sh`
mlops-initialization/SKILL.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MLOps Initialization

Goal

To initialize a robust, production-ready MLOps project structure using the modern Python toolchain (uv), industry-standard version control (git), a shared task runner (mise), and a configured development environment (VS Code). This skill ensures reproducibility, collaboration, and high code quality from day one.

Prerequisites

  • Language: Python 3.14 (latest stable)
  • Manager: uv (replaces pip, venv, poetry, pyenv)
  • Tasks: mise (replaces make/just, and pins the toolchain)
  • VCS: Git
  • IDE: VS Code (recommended)

Instructions

1. System & Toolchain Verification

Before modifying files, verify that the essential tools are available.

  1. Check uv:
    • Ensure uv is installed: uv --version
    • If missing, install it: curl -LsSf https://astral.sh/uv/install.sh | sh
  2. Check git:
    • Ensure git is installed: git --version
  3. Check mise:
    • Ensure mise is installed: mise --version
    • mise pins every non-Python tool (dprint, gitleaks, trivy, actionlint, ...) so contributors and CI resolve identical binaries.

2. Project Initialization

Initialize the project structure using uv to ensure modern standards (pyproject.toml).

  1. Create Directory (if not already inside):
    • mkdir <project_name> && cd <project_name>
  2. Initialize Project:
    • Run uv init
    • This creates pyproject.toml, .python-version, and a basic hello.py.
  3. Configure pyproject.toml:
    • Update metadata: name, version, description, authors, license.

    • Set requires-python: Ensure it matches the project's target environment (e.g., >=3.14).

    • Declare the license the PEP 639 way: license is an SPDX expression (a plain string), and the file itself is listed in license-files. The old license = { file = "LICENSE" } table form is deprecated and rejected by current build backends.

    • Example Structure:

      [project]
      name = "my-mlops-project"
      version = "0.1.0"
      description = "A robust MLOps project."
      readme = "README.md"
      requires-python = ">=3.14"
      license = "MIT" # SPDX expression (PEP 639)
      license-files = ["LICENSE.txt"] # the file(s) shipped in the distribution
      authors = [{ name = "Your Name", email = "[email protected]" }]
      dependencies = [
        "loguru>=0.7.3",
        "mlflow>=3.15.1",
        # MLflow 3.15 still declares `pandas<3`, so the pandas 3.x line is unreachable
        # here; keep the floor permissive and let `uv.lock` pin the tested version.
        "pandas>=2.3.3",
        "pydantic>=2.13.4",
        "scikit-learn>=1.9.0",
      ]
      
      [project.urls]
      Repository = "https://github.com/username/my-mlops-project"
      Documentation = "https://username.github.io/my-mlops-project"
      
      # PEP 735 dependency groups (not shipped with the package).
      [dependency-groups]
      dev = [
        "lefthook>=2.1.10",
        "pip-audit>=2.10.1",
        "pytest>=9.1.1",
        # Ruff 0.16 rewrote the default rule set and now formats Python inside Markdown:
        # an older Ruff disagrees with a 0.16-formatted repository, so floor it here.
        "ruff>=0.16.2",
        "ty>=0.0.69,<0.1", # pre-1.0: pin a compatible range
      ]
      
      [build-system]
      # Keep the upper bound at least one minor ahead of the pinned `uv`: without it
      # `uv build` warns, and a future breaking `uv_build` silently breaks the sdist.
      requires = ["uv_build>=0.9,<0.13"]
      build-backend = "uv_build"
      

Read the full file on GitHub · 212 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 212 lines · 50 tokens per session scan C 24f17b9b7010

Subscribe to this mod's changes

mlops-initialization is a skill published in the GitHub repository MLOps-Courses/mlops-coding-skills (22 stars, last pushed 22d ago), licensed MIT. It adds 50 tokens to every session and 2,776 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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