mlops-automation

mlops-automation is a skill for Claude Code, Codex from MLOps-Courses/mlops-coding-skills. It costs 58 tokens per session (2,720 once invoked), scanned A, original, MIT.

A setup for automating the checks and delivery tasks around a machine-learning Python project. It combines task commands, Git hooks, Docker, GitHub Actions, and MLflow experiment tracking.

In plain words
What is it for?
Use it to add repeatable local tasks, pre-commit checks, container builds, automated workflows, security checks, and MLflow tracking to an existing package.
Why use it?
It gives developers and continuous-integration systems shared commands for formatting, checking, testing, building, and tracking experiments.

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

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for mlops-automation

README.md
[![agentmods](https://agentmods.dev/badge/skills/mlops-courses/mlops-coding-skills/mlops-automation.svg)](https://agentmods.dev/skills/mlops-courses/mlops-coding-skills/mlops-automation)
Your own site
<a href="https://agentmods.dev/skills/mlops-courses/mlops-coding-skills/mlops-automation"><img src="https://agentmods.dev/badge/skills/mlops-courses/mlops-coding-skills/mlops-automation.svg" alt="Measured on agentmods" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,720 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 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 5.1 $0.00058 $0.02720
Opus 5 $0.00029 $0.01360
Sonnet 5 $0.00012 $0.00544
Haiku 4.5 $0.00006 $0.00272

Measured 5d ago against content hash 8fa493392ad5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

mlops-automation scanned grade A with 0 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 5d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

mlops-automation/SKILL.md · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MLOps Automation

Goal

To elevate the codebase to production standards by adding Task Automation (mise), Git Hooks (lefthook), Containerization (docker), CI/CD (github-actions), and Experiment Tracking (mlflow).

Prerequisites

  • Language: Python 3.14
  • Manager: uv
  • Context: Preparing for scale and deployment.

Instructions

1. Task Automation

Expose a single, shared task vocabulary with mise (replaces just/make).

  1. Tool: mise — pins the toolchain and defines tasks in mise.toml.
  2. Vocabulary: install, format, check, test, build, watch. Run everything via mise run <task> so hooks and CI reuse the same entrypoints.
  3. Core Tasks:
    • format: Format code and config (ruff format, dprint fmt).
    • check: Static checks in parallel (ruff check, ty, pip-audit, gitleaks, trivy, actionlint + zizmor).
    • test: Run pytest.
    • build: Build the wheel (uv build).
  4. The Gate: define all = ["mise run format", "mise run check", "mise run test", "mise run build"]. This is the one command a developer, a hook, an agent, or CI runs; nothing else is allowed to define "ready".
  5. Pinned Tools: declare every non-Python tool under [tools], run mise lock, and commit mise.lock — it records the exact version, URL, and checksum per platform, so uv.lock pins the libraries and mise.lock pins the binaries.
  6. No Silent Installs: set [settings.task] run_auto_install = false so a task fails loudly on a missing tool instead of downloading one mid-run.

2. Git Hooks

Catch issues locally with lefthook (replaces pre-commit).

  1. Framework: lefthook with thin hooks — every command delegates to a mise run task so hooks and CI stay identical.
  2. Explicit Priorities: lefthook orders commands alphabetically, so state the order yourself — formatters at 10, the staged secret scan at 20, the whole-tree checks at 30. Without priorities, check can read files that the formatter has not rewritten yet.
  3. Staged Files: pass {staged_files} to the formatters and set stage_fixed: true, so a reformat is restaged into the commit being made rather than left dirty in the working tree.
  4. Secret Gate: run mise run check:leaks --staged before the checks. A history scan does not look at the commit you are about to create; --staged does.
  5. pre-push: Run mise run test.
  6. Example:

Read the full file on GitHub · 180 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. 5d ago First seen · 180 lines · 58 tokens per session scan A 8fa493392ad5

Subscribe to this mod's changes

mlops-automation is a skill published in the GitHub repository MLOps-Courses/mlops-coding-skills (22 stars, last pushed 26d ago), licensed MIT. It adds 58 tokens to every session and 2,720 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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