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.
Quick Facts
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
# .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
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
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 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
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.