MR
Mayur Rathi
@sickn33
⭐ 47.3k GitHub stars

llmops-platform-engineering

llmops-platform-engineering is an engineering AI skill with a core value of Build production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance across cloud and self-hosted inference. It helps developers solve real-world problems in the engineering domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

Build production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance across cloud and self-hosted inference.

Last verified on: 2026-10-06

Quick Facts

Category engineering
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 47.3k
Last Verified 2026-10-06
Risk Level Low
mkdir -p ./skills/llmops-platform-engineering && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/llmops-platform-engineering/SKILL.md -o ./skills/llmops-platform-engineering/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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"
        required: true
        type: choice
        options: [staging, production]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run quality evaluation suite
        run: |
          python -m evals.run \
            --model "${{ inputs.model_name }}:${{ inputs.model_version }}" \
            --suite quality \
            --output results/quality.json

      - name: Run safety evaluation suite
        run: |
          python -m evals.run \
            --model "${{ inputs.model_name }}:${{ inputs.model_version }}" \
            --suite safety \
            --output results/safety.json

      - name: Run latency benchmark
        run: |
          python -m evals.benchmark \
            --model "${{ inputs.model_name }}:${{ inputs.model_version }}" \
            --concurrent-users 50 \
            --duration 300 \
            --output results/latency.json

      - name: Gate check - quality
        run: |
          python -m evals.gate_check \
            --results results/quality.json \
            --threshold-file thresholds/quality.yaml

      - name: Gate check - safety
        run: |
          python -m evals.gate_check \
            --results results/safety.json \
            --threshold-file thresholds/safety.yaml

      - name: Gate check - latency
        run: |
          python -m evals.gate_check \
            --results results/latency.json \
            --threshold-file thresholds/latency.yaml

      - name: Upload eval evidence
        uses: actions/upload-artifact@v4
        with:
          name: eval-results-${{ inputs.model_version }}
          path: results/

  approve:
    needs: evaluate
    runs-on: ubuntu-latest
    environment: ${{ inputs.target_env }}
    steps:
      - name: Record approval
        run: |
          echo "Approved by: ${{ github.actor }}"
          echo "Model: ${{ inputs.model_name }}:${{ inputs.model_version }}"
          echo "Target: ${{ inputs.target_env }}"
          echo "Time: $(date -u +%Y-%m-%dT%H:%M:%SZ)"

  deploy:
    needs: approve
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Deploy canary
        ru

🎯 Best For

  • UI designers
  • Product designers
  • Claude users
  • AI users

💡 Use Cases

  • Generating component mockups
  • Creating design system tokens
  • Using llmops-platform-engineering in daily workflow
  • Automating repetitive engineering tasks

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply llmops-platform-engineering to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does this work with Figma?

Some design skills integrate with Figma plugins. Check the Works With section for supported tools.

How do I install llmops-platform-engineering?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/llmops-platform-engineering/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Skipping usability testing

AI-generated designs should be validated with real users before development.

Not reading the full skill

Skills contain important context and edge cases beyond the quick start.

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