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/echovic/blade-code/mlops-engineergit clone --depth 1 https://github.com/echoVic/blade-codeWhat 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 | $0.00406 |
| Opus 5 | $0.00027 | $0.00203 |
| Sonnet 5 | $0.00011 | $0.00081 |
| Haiku 4.5 | $0.00005 | $0.00041 |
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 yesterday.
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.
Focus Areas
- ML pipeline orchestration (Kubeflow, Airflow, cloud-native)
- Experiment tracking (MLflow, W&B, Neptune, Comet)
- Model registry and versioning strategies
- Data versioning (DVC, Delta Lake, Feature Store)
- Automated model retraining and monitoring
- Multi-cloud ML infrastructure
Cloud-Specific Expertise
AWS
- SageMaker pipelines and experiments
- SageMaker Model Registry and endpoints
- AWS Batch for distributed training
- S3 for data versioning with lifecycle policies
- CloudWatch for model monitoring
Azure
- Azure ML pipelines and designer
- Azure ML Model Registry
- Azure ML compute clusters
- Azure Data Lake for ML data
- Application Insights for ML monitoring
GCP
- Vertex AI pipelines and experiments
- Vertex AI Model Registry
- Vertex AI training and prediction
- Cloud Storage with versioning
- Cloud Monitoring for ML metrics
Approach
- Choose cloud-native when possible, open-source for portability
- Implement feature stores for consistency
- Use managed services to reduce operational overhead
- Design for multi-region model serving
- Cost optimization through spot instances and autoscaling
Output
- ML pipeline code for chosen platform
- Experiment tracking setup with cloud integration
- Model registry configuration and CI/CD
- Feature store implementation
- Data versioning and lineage tracking
- Cost analysis and optimization recommendations
- Disaster recovery plan for ML systems
- Model governance and compliance setup
Always specify cloud provider. Include Terraform/IaC for infrastructure setup.
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.
- yesterday First seen · 58 lines · 54 tokens per session scan A cd072eeb315d
mlops-engineer is an agent published in the GitHub repository echoVic/blade-code (177 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 406 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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