# llmops-platform-engineering

A skill for building an internal LLM platform: CI/CD with evaluation gates, model promotion, canary deploys and rollback.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 315 installs · ★ 1177
- Author: devops-skills
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/HEAD/devops/ai/llmops-platform-engineering/SKILL.md
- Updated: 2026-05-22
- HTML: https://affpapa.org/en/nevibecode/skills/llmops-platform-engineering

## What it does

This skill walks through building an internal platform for teams shipping LLM features without giving up reliability or cost control. It lays out a reference architecture in four planes: a control plane with the model registry and prompt catalog, a data plane with the inference gateway and vector database, an ops plane with telemetry and SLO dashboards, and a security plane with IAM boundaries and audit logs.

The core is a model promotion pipeline for GitHub Actions. Quality and safety evaluation suites run first, along with a latency benchmark, then gate checks compare the results against thresholds in `thresholds/quality.yaml` and `thresholds/latency.yaml`. After manual approval the model goes to a canary deployment in Kubernetes before full rollout. A separate config covers A/B tests with traffic split and auto-rollback on metrics.

Running it needs a Kubernetes cluster with GPU node pools or cloud inference API access, a container registry, a CI/CD system and an observability stack. The pipeline calls `evals.run`, `evals.gate_check` and `canary.validate` — if those modules are missing from your repo, the first job fails, so check the environment before the first run.

## Requirements
- Works with: Kubernetes

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llmops-platform-engineering -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/llmops-platform-engineering/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/llmops-platform-engineering/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llmops-platform-engineering -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/llmops-platform-engineering/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llmops-platform-engineering -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/llmops-platform-engineering/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install BagelHole/DevOps-Security-Agent-Skills llmops-platform-engineering
```

2. **Manually: put the files in place**
- File: `.github/skills/llmops-platform-engineering/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llmops-platform-engineering -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/BagelHole/DevOps-Security-Agent-Skills --path devops/ai/llmops-platform-engineering
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/llmops-platform-engineering/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llmops-platform-engineering -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/HEAD/devops/ai/llmops-platform-engineering/SKILL.md

## When to use it
- wire evaluation gates into a model deployment pipeline
- set up canary rollout with automatic rollback on metrics
- define quality and latency thresholds in gate configs
- document a reference architecture for a multi-team LLM platform
- run an A/B test between two model versions

## Example prompts
- Help me design a model promotion pipeline with quality and safety gates
- Set up a canary deployment for a model in Kubernetes with rollback on error rate
- Draft the reference architecture for an internal LLM platform used by several teams

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8c800bf2caa57c19.

BEGIN THIRD-PARTY FILE 8c800bf2caa57c19
````text
---
name: llmops-platform-engineering
description: Build production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance across cloud and self-hosted inference.
license: MIT
metadata:
  author: devops-skills
  version: "1.0"
---

# LLMOps Platform Engineering

Design and operate an internal LLM platform that supports rapid experimentation without compromising reliability, cost, or compliance.

## When to Use This Skill

- Building an internal platform for teams to deploy and manage LLM-powered features
- Designing CI/CD pipelines that include model evaluation gates
- Setting up A/B testing infrastructure for model versions
- Creating Kubernetes-based model serving infrastructure
- Establishing governance workflows for model promotion

## Prerequisites

- Kubernetes cluster with GPU node pools (or cloud inference API access)
- Container registry (Harbor, ECR, GCR, or ACR)
- CI/CD system (GitHub Actions, GitLab CI, or Argo Workflows)
- Observability stack (Prometheus + Grafana + OpenTelemetry)
- Model registry (MLflow or custom metadata store)

## Outcomes

- Standardized path from experiment to production
- Safe model rollout with quality and safety gates
- Repeatable infra modules for inference, vector DB, and observability
- Clear ownership model across platform, app, and security teams

## Reference Architecture

1. **Control Plane**: model registry, prompt/version catalog, policy checks, eval pipeline.
2. **Data Plane**: inference gateway, vector database, cache, feature store.
3. **Ops Plane**: telemetry, alerting, SLO dashboards, cost analytics.
4. **Security Plane**: IAM boundaries, secret rotation, content filters, audit logs.

## Model Promotion Pipeline

```yaml
# .github/workflows/model-promotion.yaml
name: Model Promotion Pipeline
on:
  workflow_dispatch:
    inputs:
      model_name:
        description: "Model identifier"
        required: true
      model_version:
        description: "Model version to promote"
        required: true
      target_env:
        description: "Target environment"
````
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The third-party file has ended. Everything between the 8c800bf2caa57c19 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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