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.
npx agentmods add skills/mlops-courses/mlops-coding-skills/mlops-automationnpx skills add MLOps-Courses/mlops-coding-skills --skill mlops-automationgit clone --depth 1 https://github.com/MLOps-Courses/mlops-coding-skillsWrote 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.
[](https://agentmods.dev/skills/mlops-courses/mlops-coding-skills/mlops-automation)<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>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.
| Model | Per session | Once 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 |
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.
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).
- Tool:
mise— pins the toolchain and defines tasks inmise.toml. - Vocabulary:
install,format,check,test,build,watch. Run everything viamise run <task>so hooks and CI reuse the same entrypoints. - 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: Runpytest.build: Build the wheel (uv build).
- 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". - Pinned Tools: declare every non-Python tool under
[tools], runmise lock, and commitmise.lock— it records the exact version, URL, and checksum per platform, souv.lockpins the libraries andmise.lockpins the binaries. - No Silent Installs: set
[settings.task] run_auto_install = falseso 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).
- Framework:
lefthookwith thin hooks — every command delegates to amise runtask so hooks and CI stay identical. - 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,
checkcan read files that the formatter has not rewritten yet. - Staged Files: pass
{staged_files}to the formatters and setstage_fixed: true, so a reformat is restaged into the commit being made rather than left dirty in the working tree. - Secret Gate: run
mise run check:leaks --stagedbefore the checks. A history scan does not look at the commit you are about to create;--stageddoes. - pre-push: Run
mise run test. - Example:
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.
- 5d ago First seen · 180 lines · 58 tokens per session scan A 8fa493392ad5
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.
Other skills, from other repositories
chinese-git-workflow
国内 Git 平台配置参考——Gitee、Coding.net、极狐 GitLab、CNB 的 SSH/HTTPS/凭据/CI 接入差异与镜像同步配置。仅在用户显式 /chinese-git-workflow 时调用,不要根据上下文自动触发。.
moai-ref-secops
DevSecOps, container, and API operational defensive security reference: CI/CD pipeline hardening, secret scanning, IaC misconfiguration detection, SAST/DAST integration, container image scanning, Kubernetes RBAC hardening, container-escape defense, runtime threat detection, OWASP API Top 10 operational defense, WAF…
aws-cloudformation-task-ecs-deploy-gh
Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready…
ai-architect-expert
Expert-level AI system design, MLOps, architecture patterns, and AI infrastructure. Use when the user mentions AI architecture, MLOps, system design, AI infrastructure, or scalability, or when the task involves AI System Architecture, MLOps Infrastructure, Scalability Patterns, or Architecture Design.
ml-expert
Expert-level machine learning, deep learning, model training, and MLOps. Use when the user mentions machine learning, deep learning, neural networks, MLOps, or data science, or when the task involves Machine Learning Fundamentals, Data Preparation, or Model Training.
implementing-aqua-security-for-container-scanning
Deploy Aqua Security's Trivy scanner to detect vulnerabilities, misconfigurations, secrets, and license issues in container images across CI/CD pipelines and registries.