mlops-deployment
Facilitates the deployment and monitoring of ML models using Docker, Kubernetes, and CI/CD pipelines for robust production infrastructure.
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The mlops-deployment skill was audited on Mar 3, 2026 and we found 4 security issues across 3 threat categories. Review the findings below before installing.
Categories Tested
Security Issues
medium line 128
Template literal with variable interpolation in command context
SourceSKILL.md
| 128 | ```yaml |
medium line 34
Curl to non-GitHub URL
SourceSKILL.md
| 34 | CMD curl -f http://localhost:8000/health || exit 1 |
low line 34
External URL reference
SourceSKILL.md
| 34 | CMD curl -f http://localhost:8000/health || exit 1 |
low line 248
External URL reference
SourceSKILL.md
| 248 | mlflow.set_tracking_uri("http://localhost:5000") |
Scanned on Mar 3, 2026
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Categorydevelopment
UpdatedMay 21, 2026
openclawbackenddevopsml-ai-engineerdata-scientistdevops-srebackend-developerproduct-managerdockerkubernetesgithubdevelopmentdata analyticsproduct
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