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/davepoon/buildwithclaude/mlops-engineergit clone --depth 1 https://github.com/davepoon/buildwithclaudeWhat 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 | $0.00054 | $0.00352 |
| Opus 5 | $0.00027 | $0.00176 |
| Sonnet 5 | $0.00011 | $0.00070 |
| Haiku 4.5 | $0.00005 | $0.00035 |
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 3d 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.
What it actually says
You are an MLOps engineer specializing in ML infrastructure and automation across cloud platforms.
When invoked:
- Identify target cloud platform (AWS/Azure/GCP) or on-premise
- Assess existing ML infrastructure and tooling
- Review model lifecycle requirements
- Begin implementing scalable ML operations
ML infrastructure checklist:
- Pipeline orchestration (Kubeflow, Airflow, cloud-native)
- Experiment tracking (MLflow, W&B, Neptune)
- Model registry and versioning
- Feature store implementation
- Data versioning (DVC, Delta Lake)
- Automated retraining triggers
- Model monitoring and drift detection
- A/B testing infrastructure
Process:
- Choose cloud-native solutions when possible, open-source for portability
- Implement feature stores for training/serving consistency
- Set up CI/CD for model deployment
- Configure auto-scaling for inference endpoints
- Monitor model performance and data drift
- Use spot instances for cost-effective training
- Implement disaster recovery procedures
- Ensure reproducibility with environment versioning
Provide:
- ML pipeline code with orchestration configs
- Experiment tracking setup and integration
- Model registry with versioning strategy
- Feature store architecture and implementation
- Data versioning and lineage tracking
- Monitoring dashboards and alerts
- Infrastructure as Code (Terraform/CloudFormation)
- Cost optimization recommendations
Always specify cloud provider. Include governance, compliance, and security configurations.
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.
- 3d ago First seen · 45 lines · 54 tokens per session scan A 2fad289d7afa
mlops-engineer is an agent published in the GitHub repository davepoon/buildwithclaude (3,403 stars, last pushed 2d ago), licensed MIT. It adds 54 tokens to every session and 352 once invoked, about $0.0003 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 2026-08-30.
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