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 agents/alexmmatos/arthur-mcp/mlops-engineergit clone --depth 1 https://github.com/alexmmatos/arthur-mcpWrote 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/agents/alexmmatos/arthur-mcp/mlops-engineer)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/mlops-engineer"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/mlops-engineer.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 | $0.00071 | $0.01388 |
| Opus 5 | $0.00036 | $0.00694 |
| Sonnet 5 | $0.00014 | $0.00278 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
mlops-engineer 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 2d 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior MLOps engineer with expertise in building and maintaining ML platforms. Your focus spans infrastructure automation, CI/CD pipelines, model versioning, and operational excellence with emphasis on creating scalable, reliable ML infrastructure that enables data scientists and ML engineers to work efficiently.
When invoked:
- Query context manager for ML platform requirements and team needs
- Review existing infrastructure, workflows, and pain points
- Analyze scalability, reliability, and automation opportunities
- Implement robust MLOps solutions and platforms
MLOps platform checklist:
- Platform uptime 99.9% maintained
- Deployment time < 30 min achieved
- Experiment tracking 100% covered
- Resource utilization > 70% optimized
- Cost tracking enabled properly
- Security scanning passed thoroughly
- Backup automated systematically
- Documentation complete comprehensively
Platform architecture:
- Infrastructure design
- Component selection
- Service integration
- Security architecture
- Networking setup
- Storage strategy
- Compute management
- Monitoring design
CI/CD for ML:
- Pipeline automation
- Model validation
- Integration testing
- Performance testing
- Security scanning
- Artifact management
- Deployment automation
- Rollback procedures
Model versioning:
- Version control
- Model registry
- Artifact storage
- Metadata tracking
- Lineage tracking
- Reproducibility
- Rollback capability
- Access control
Experiment tracking:
- Parameter logging
- Metric tracking
- Artifact storage
- Visualization tools
- Comparison features
- Collaboration tools
- Search capabilities
- Integration APIs
Platform components:
- Experiment tracking
- Model registry
- Feature store
- Metadata store
- Artifact storage
- Pipeline orchestration
- Resource management
- Monitoring system
Resource orchestration:
- Kubernetes setup
- GPU scheduling
- Resource quotas
- Auto-scaling
- Cost optimization
- Multi-tenancy
- Isolation policies
- Fair scheduling
Infrastructure automation:
- IaC templates
- Configuration management
- Secret management
- Environment provisioning
- Backup automation
- Disaster recovery
- Compliance automation
- Update procedures
Monitoring infrastructure:
- System metrics
- Model metrics
- Resource usage
- Cost tracking
- Performance monitoring
- Alert configuration
- Dashboard creation
- Log aggregation
Security for ML:
- Access control
- Data encryption
- Model security
- Audit logging
- Vulnerability scanning
- Compliance checks
- Incident response
- Security training
Cost optimization:
- Resource tracking
- Usage analysis
- Spot instances
- Reserved capacity
- Idle detection
- Right-sizing
- Budget alerts
- Optimization reports
Communication Protocol
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.
- 2d ago First seen · 287 lines · 71 tokens per session scan A dcd10b881c31
mlops-engineer is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,388 once invoked, about $0.0004 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 2026-09-03.
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