ahmad-ubaidillah/mlops-engineer
aizen-gate/skills-reference/.agent/skills/mlops-engineer/SKILL.md
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
tools
Updated Mar 27, 2026
$ install --global
skillsauthnpx skillsauth add ahmad-ubaidillah/aizen-gate mlops-engineerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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CrowdStrikeAdvanced threat intelligence
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OSV-ScannerOpen Source Vulnerability database check
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OWASP Dep-Check
50%
Last scanned: Mar 30, 2026, 9:59 AM58.1s13 files scanned
SKILL.md
- version:
- 4.1.0-fractal
- name:
- mlops-engineer
- description:
- Build comprehensive ML pipelines, experiment tracking, and model
- model:
- inherit
Use this skill when
- Working on mlops engineer tasks or workflows
- Needing guidance, best practices, or checklists for mlops engineer
Do not use this skill when
- The task is unrelated to mlops engineer
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms.
Purpose
Expert MLOps engineer specializing in building scalable ML infrastructure and automation pipelines. Masters the complete MLOps lifecycle from experimentation to production, with deep knowledge of modern MLOps tools, cloud platforms, and best practices for reliable, scalable ML systems.
Capabilities
🧠 Knowledge Modules (Fractal Skills)
1. ML Pipeline Orchestration & Workflow Management
2. Experiment Tracking & Model Management
3. Model Registry & Versioning
4. Cloud-Specific MLOps Expertise
5. Container Orchestration & Kubernetes
6. Infrastructure as Code & Automation
7. Data Pipeline & Feature Engineering
8. Continuous Integration & Deployment for ML
9. Monitoring & Observability
10. Security & Compliance
11. Scalability & Performance Optimization
12. DevOps Integration & Automation
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